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@article{shannon_entropy,
	Author = {Shannon, Claude E},
	Date-Added = {2020-10-07 11:08:19 -0700},
	Date-Modified = {2020-10-07 11:10:38 -0700},
	Journal = {Bell System Technical Journal},
	Number = {3},
	Pages = {379-423},
	Title = {A mathematical theory of communication},
	Volume = {27},
	Year = {1948}}

@article{hainmueller2012entropy,
	Author = {Hainmueller, Jens},
	Date-Added = {2020-10-07 10:55:44 -0700},
	Date-Modified = {2020-10-07 10:56:43 -0700},
	Journal = {Political Analysis},
	Number = {1},
	Pages = {25-46},
	Title = {Entropy balancing for causal effects: {A} multivariate reweighting method to produce balanced samples in observational studies},
	Volume = {20},
	Year = {2012}}

@book{fan_local_polynomial,
	Address = {London},
	Author = {Jianqing Fan and Irene Gijbels},
	Date-Added = {2020-10-07 10:00:45 -0700},
	Date-Modified = {2020-10-07 10:02:09 -0700},
	Publisher = {Champan and Hall},
	Title = {Local Polynomial Modeling and Its Applications},
	Year = {1996}}

@book{pearl_book_of_why,
	Address = {New York},
	Author = {Judea Pearl and Dana Mackenzie},
	Date-Added = {2020-09-20 15:58:54 -0700},
	Date-Modified = {2020-09-20 16:03:19 -0700},
	Publisher = {Basic Books},
	Title = {The Book of Why: The New Science of Cause and Effect},
	Year = {2018}}

@article{nobari2018trends,
	Author = {Nobari, Tabashir Z and Whaley, Shannon E and Prelip, Michael L and Crespi, Catherine M and Wang, May C},
	Date-Added = {2020-08-18 21:31:13 -0700},
	Date-Modified = {2020-08-22 00:02:24 -0700},
	Journal = {Childhood Obesity},
	Number = {4},
	Pages = {248-258},
	Title = {Trends in socioeconomic disparities in obesity prevalence among low-income children aged 2--4 years in {Los Angeles County}, 2003--2014},
	Volume = {14},
	Year = {2018}}

@article{Chaparro:2019aa,
	Abstract = {BACKGROUND: In 2009, for the first time since the program's inception in 1974, the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) changed their food packages, providing food options better aligned with the 2005 Dietary Guidelines for Americans. OBJECTIVE: The aim of this study was to evaluate whether the 2009 WIC food package change was associated with changes in growth trajectories from age 0 to 4 y or obesity at age 4 among children who participated in WIC in Los Angeles County between 2003 and 2016. METHODS: Children were grouped into 1 of 4 exposure groups: full-dose, new food package group (participating in WIC from birth to age 4, post 2009, N = 70,120), full-dose, old food package group (participating from birth to age 4, pre 2009, N = 85,871), late-dose, new food package group (participating from age 2 to 4 y, post 2009, N = 8386), and late-dose, old food package group (participating from age 2 to 4 y, pre 2009, N = 18,241). Children were matched across groups on gender, race/ethnicity, maternal education and language, family income, and initial weight status, and matched analyses were performed. Longitudinal growth trajectories were modeled using piecewise linear spline mixed models, and differences in obesity at age 4 were compared using Poisson regression models. RESULTS: Children receiving a full dose of the new food package had healthier growth trajectories and a lower obesity risk at age 4 than children receiving a full dose of the old food package (RR [95% CI]: 0.88 [0.86, 0.91] for boys, 0.90 [0.87, 0.93] for girls). Boys, but not girls, in the late-dose, new food package group had a lower obesity risk at age 4 compared with boys in the late-dose, old food package group (RR = 0.89, 95% CI = 0.81, 0.98). CONCLUSIONS: The WIC food package change appears to be associated with improved childhood obesity outcomes. These findings are important in informing policymakers considering further improvements to the WIC food packages.},
	Author = {Chaparro, M Pia and Crespi, Catherine M and Anderson, Christopher E and Wang, May C and Whaley, Shannon E},
	Date-Added = {2020-08-18 21:03:41 -0700},
	Date-Modified = {2020-08-18 21:04:42 -0700},
	Journal = {Am J Clin Nutr},
	Number = {5},
	Pages = {1414--1421},
	Title = {{The 2009 Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food package change and children's growth trajectories and obesity in Los Angeles County}},
	Volume = {109},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1093/ajcn/nqy347}}

@article{hiv_combo_tx,
	Abstract = {Analysis of changes in viral load after initiation of treatment with potent antiretroviral agents has provided substantial insight into the dynamics of human immunodeficiency virus type 1 (HIV-1)1--3. The concentration of HIV-1 in plasma drops by ∼99{\%} in the first two weeks of treatment owing to the rapid elimination of free virus with a half-life (t1/2) of ≤6 hours and loss of productively infected cells with a t1/2 of 1.6 days3. Here we show that with combination therapy this initial decrease is followed by a slower second-phase decay of plasma viraemia. Detailed mathematical analysis shows that the loss of long-lived infected cells (t1/2of1--4weeks) is a major contributor to the second phase, whereas the activation of latently infected lymphocytes (t1/2 of 0.5--2 weeks) is only a minor source. Based on these decay characteristics, we estimate that 2.3--3.1 years of a completely inhibitory treatment would be required to eliminate HIV-1 from these compartments. To eradicate HIV-1 completely, even longer treatment may be needed because of the possible existence of undetected viral compartments or sanctuary sites.},
	Author = {Perelson, Alan S. and Essunger, Paulina and Cao, Yunzhen and Vesanen, Mika and Hurley, Arlene and Saksela, Kalle and Markowitz, Martin and Ho, David D.},
	Date-Added = {2020-08-04 20:15:42 -0700},
	Date-Modified = {2020-08-04 20:17:30 -0700},
	Journal = {Nature},
	Number = {6629},
	Pages = {188-191},
	Title = {{Decay characteristics of HIV-1-infected compartments during combination therapy}},
	Volume = {387},
	Year = {1997},
	Bdsk-Url-1 = {https://doi.org/10.1038/387188a0}}

@article{mecklin2004,
	Abstract = {Summary This paper is a review of many of the dozens of procedures currently available for testing a data set for goodness-of-fit to the multivariate normal distribution. A majority of the procedures can be placed into one of four basic categories. Most procedures are multivariate extensions or adaptations of procedures used for testing univariate normality. Results of several power studies are summarized, and an extensive bibliography of literature pertaining to testing for multivariate normality is provided.},
	Author = {Mecklin, Christopher J. and Mundfrom, Daniel J.},
	Date-Added = {2020-07-25 20:14:03 -0700},
	Date-Modified = {2020-08-21 23:55:50 -0700},
	Journal = {International Statistical Review},
	Number = {1},
	Pages = {123-138},
	Title = {An Appraisal and Bibliography of Tests for Multivariate Normality},
	Volume = {72},
	Year = {2004},
	Bdsk-Url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-5823.2004.tb00228.x},
	Bdsk-Url-2 = {https://doi.org/10.1111/j.1751-5823.2004.tb00228.x}}

@article{anderson2020neighborhood,
	Author = {Anderson, Christopher E and Crespi, Catherine M and Wang, May C and Whaley, Shannon E and Chaparro, M Pia},
	Date-Added = {2020-07-24 11:00:57 -0700},
	Date-Modified = {2020-07-24 11:02:06 -0700},
	Journal = {{BMC Public Health}},
	Number = {678},
	Pages = {1-11},
	Title = {{The neighborhood food environment modifies the effect of the 2009 WIC food package change on childhood obesity in Los Angeles County, California}},
	Volume = {20},
	Year = {2020}}

@article{Wang2006aa,
	Abstract = {With the rapidly increasing prevalence of obesity in the United States, and the minimal success of education-based interventions, there is growing interest in understanding the role of the neighborhood food environment in determining dietary behavior. This study, as part of a larger study, identifies historical data on retail food stores, evaluates strengths and limitations of the data for research, and assesses the comparability of historical retail food store data from a government and a commercial source.},
	Author = {Wang, May C. and Gonzalez, Alma A. and Ritchie, Lorrene D. and Winkleby, Marilyn A.},
	Date-Added = {2020-07-24 10:53:52 -0700},
	Date-Modified = {2020-08-22 15:54:53 -0700},
	Journal = {{International Journal of Behavioral Nutrition and Physical Activity}},
	Number = {1},
	Pages = {15},
	Title = {The neighborhood food environment: {S}ources of historical data on retail food stores},
	Volume = {3},
	Year = {2006},
	Bdsk-Url-1 = {https://doi.org/10.1186/1479-5868-3-15}}

@article{Nobari2018aa,
	Abstract = {OBJECTIVE: While economic crises can increase socio-economic disparities in health, little is known about the impact of the 2008-09 Great Recession on obesity prevalence among children, especially low-income children. The present study examined whether socio-economic disparities in obesity among children of pre-school age participating in a federal nutrition assistance programme have changed since the recession. DESIGN: A pre-post observational study using administrative data of pre-school-aged programme participants from 2003 to 2014. Logistic regression was used to examine whether the relationship between obesity prevalence (BMI≥95th percentile of the Centers for Disease Control and Prevention's growth charts) and three measures of socio-economic status (household income, household educational attainment, neighbourhood-level median household income) changed after the recession by examining the interaction between each socio-economic status measure and a 5-year time-period variable (2003-07 v. 2010-14), stratified by child's age and adjusted for child's sociodemographic characteristics. SETTING: Los Angeles County, California, USA. SUBJECTS: Children aged 2-4 years (n 1 637 788) participating in the Special Supplemental Nutrition Program for Women, Infants, and Children. RESULTS: The magnitude of the association of household income and household education with obesity increased after 2008-09 among 3- and 4-year-olds and 2- and 3-year-olds, respectively. However, the magnitude of the association of neighbourhood-level median household income with obesity did not change after 2008-09. CONCLUSIONS: Disparities in obesity by household-level socio-economic status widened after the recession, while disparities by neighbourhood-level socio-economic status remained the same. The widening household-level socio-economic disparities suggest that obesity prevention efforts should target the most vulnerable low-income children.},
	Author = {Nobari, Tabashir Z and Whaley, Shannon E and Crespi, Catherine M and Prelip, Michael L and Wang, May C},
	Date-Added = {2020-07-24 10:46:56 -0700},
	Date-Modified = {2020-08-22 00:01:16 -0700},
	Journal = {Public Health Nutrition},
	Number = {12},
	Pages = {2301-2310},
	Title = {Widening socio-economic disparities in early childhood obesity in {Los Angeles County} after the Great Recession.},
	Volume = {21},
	Year = {2018}}

@article{rosenkranz2008,
	Abstract = {{The home food environment can be conceptualized as overlapping interactive domains composed of built and natural, sociocultural, political and economic, micro-level and macro-level environments. Each type and level of environment uniquely contributes influence through a mosaic of determinants depicting the home food environment as a major setting for shaping child dietary behavior and the development of obesity. Obesity is a multifactorial problem, and the home food environmental aspects described here represent a substantial part of the full environmental context in which a child grows, develops, eats, and behaves. The present review includes selected literature relevant to the home food environment's influence on obesity with the aim of presenting an ecologically informed model for future research and intervention in the home food environment.}},
	Author = {Rosenkranz, Richard R and Dzewaltowski, David A},
	Date-Added = {2020-07-23 14:16:46 -0700},
	Date-Modified = {2020-07-23 14:17:21 -0700},
	Journal = {Nutrition Reviews},
	Number = {3},
	Pages = {123-140},
	Title = {{Model of the home food environment pertaining to childhood obesity}},
	Volume = {66},
	Year = {2008},
	Bdsk-Url-1 = {https://doi.org/10.1111/j.1753-4887.2008.00017.x}}

@article{chazelle1993optimal,
	Author = {Chazelle, Bernard},
	Date-Added = {2020-07-13 15:16:59 -0700},
	Date-Modified = {2020-07-13 15:17:14 -0700},
	Journal = {{Discrete \& Computational Geometry}},
	Number = {4},
	Pages = {377-409},
	Publisher = {Springer},
	Title = {An optimal convex hull algorithm in any fixed dimension},
	Volume = {10},
	Year = {1993}}

@article{covid_combo_gautret2,
	Abstract = {Background
We need an effective treatment to cure COVID-19 patients and to decrease virus carriage duration.
Methods
We conducted an uncontrolled, non-comparative, observational study in a cohort of 80 relatively mildly infected inpatients treated with a combination of hydroxychloroquine and azithromycin over a period of at least three days, with three main measurements: clinical outcome, contagiousness as assessed by PCR and culture, and length of stay in infectious disease unit (IDU).
Results
All patients improved clinically except one 86 year-old patient who died, and one 74 year-old patient still in intensive care. A rapid fall of nasopharyngeal viral load was noted, with 83% negative at Day7, and 93% at Day8. Virus cultures from patient respiratory samples were negative in 97.5% of patients at Day5. Consequently patients were able to be rapidly discharged from IDU with a mean length of stay of five days.
Conclusion
We believe there is urgency to evaluate the effectiveness of this potentially-life saving therapeutic strategy at a larger scale, both to treat and cure patients at an early stage before irreversible severe respiratory complications take hold and to decrease duration of carriage and avoid the spread of the disease. Furthermore, the cost of treatment is negligible.},
	Author = {Philippe Gautret and Jean-Christophe Lagier and Philippe Parola and Van Thuan Hoang and Line Meddeb and Jacques Sevestre and Morgane Mailhe and Barbara Doudier and Camille Aubry and Sophie Amrane and Piseth Seng and Marie Hocquart and Carole Eldin and Julie Finance and Vera Esteves Vieira and Herv{\'e} Tissot Tissot-Dupont and St{\'e}phane Honor{\'e} and Andreas Stein and Matthieu Million and Philippe Colson and Bernard {La Scola} and V{\'e}ronique Veit and Alexis Jacquier and Jean-Claude Deharo and Michel Drancourt and Pierre Edouard Fournier and Jean-Marc Rolain and Philippe Brouqui and Didier Raoult},
	Date-Added = {2020-07-11 22:04:53 -0700},
	Date-Modified = {2020-08-21 23:41:30 -0700},
	Journal = {{Travel Medicine and Infectious Disease}},
	Pages = {101663},
	Title = {{Clinical and microbiological effect of a combination of hydroxychloroquine and azithromycin in 80 COVID-19 patients with at least a six-day follow up: A pilot observational study}},
	Url = {https://doi.org/10.1016/j.tmaid.2020.101663},
	Volume = {34},
	Year = {2020},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1477893920301319},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.tmaid.2020.101663}}

@article{covid_tx_review,
	Abstract = {{The pandemic of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents an unprecedented challenge to identify effective drugs for prevention and treatment. Given the rapid pace of scientific discovery and clinical data generated by the large number of people rapidly infected by SARS-CoV-2, clinicians need accurate evidence regarding effective medical treatments for this infection.No proven effective therapies for this virus currently exist. The rapidly expanding knowledge regarding SARS-CoV-2 virology provides a significant number of potential drug targets. The most promising therapy is remdesivir. Remdesivir has potent in vitro activity against SARS-CoV-2, but it is not US Food and Drug Administration approved and currently is being tested in ongoing randomized trials. Oseltamivir has not been shown to have efficacy, and corticosteroids are currently not recommended. Current clinical evidence does not support stopping angiotensin-converting enzyme inhibitors or angiotensin receptor blockers in patients with COVID-19.The COVID-19 pandemic represents the greatest global public health crisis of this generation and, potentially, since the pandemic influenza outbreak of 1918. The speed and volume of clinical trials launched to investigate potential therapies for COVID-19 highlight both the need and capability to produce high-quality evidence even in the middle of a pandemic. No therapies have been shown effective to date.}},
	Author = {Sanders, James M. and Monogue, Marguerite L. and Jodlowski, Tomasz Z. and Cutrell, James B.},
	Date-Added = {2020-07-11 22:03:09 -0700},
	Date-Modified = {2020-08-22 15:42:45 -0700},
	Journal = {JAMA},
	Number = {18},
	Pages = {1824-1836},
	Title = {Pharmacologic Treatments for Coronavirus Disease 2019 {(COVID-19)}: {A} Review},
	Volume = {323},
	Year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1001/jama.2020.6019}}

@article{covid_combo_gautret,
	Abstract = {Background
Chloroquine and hydroxychloroquine have been found to be efficient on SARS-CoV-2, and reported to be efficient in Chinese COV-19 patients. We evaluate the role of hydroxychloroquine on respiratory viral loads.
Patients and methods
French Confirmed COVID-19 patients were included in a single arm protocol from early March to March 16th, to receive 600mg of hydroxychloroquine daily and their viral load in nasopharyngeal swabs was tested daily in a hospital setting. Depending on their clinical presentation, azithromycin was added to the treatment. Untreated patients from another center and cases refusing the protocol were included as negative controls. Presence and absence of virus at Day6-post inclusion was considered the end point.
Results
Six patients were asymptomatic, 22 had upper respiratory tract infection symptoms and eight had lower respiratory tract infection symptoms. Twenty cases were treated in this study and showed a significant reduction of the viral carriage at D6-post inclusion compared to controls, and much lower average carrying duration than reported of untreated patients in the literature. Azithromycin added to hydroxychloroquine was significantly more efficient for virus elimination.
Conclusion
Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.},
	Author = {Philippe Gautret and Jean-Christophe Lagier and Philippe Parola and Van Thuan Hoang and Line Meddeb and Morgane Mailhe and Barbara Doudier and Johan Courjon and Val{\'e}rie Giordanengo and Vera Esteves Vieira and Herv{\'e} Tissot Dupont and St{\'e}phane Honor{\'e} and Philippe Colson and Eric Chabri{\`e}re and Bernard {La Scola} and Jean-Marc Rolain and Philippe Brouqui and Didier Raoult},
	Date-Added = {2020-07-11 21:59:33 -0700},
	Date-Modified = {2020-08-21 23:41:54 -0700},
	Journal = {International Journal of Antimicrobial Agents},
	Title = {{Hydroxychloroquine and azithromycin as a treatment of COVID-19: Results of an open-label non-randomized clinical trial}},
	Url = {https://doi.org/10.1016/j.ijantimicag.2020.105949},
	Year = {2020},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0924857920300996},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.ijantimicag.2020.105949}}

@article{covid_combo_stebbing,
	Annote = {doi: 10.1016/S1473-3099(20)30132-8},
	Author = {Stebbing, Justin and Phelan, Anne and Griffin, Ivan and Tucker, Catherine and Oechsle, Olly and Smith, Dan and Richardson, Peter},
	Date-Added = {2020-07-11 21:55:04 -0700},
	Date-Modified = {2020-08-22 15:44:02 -0700},
	Journal = {The Lancet Infectious Diseases},
	Number = {4},
	Pages = {400-402},
	Title = {{COVID-19}: Combining antiviral and anti-inflammatory treatments},
	Volume = {20},
	Year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1016/S1473-3099(20)30132-8}}

@article{CBGPS_time_varying,
	Author = {Curtis Huffman and Edwin van Gameren},
	Date-Added = {2020-07-11 21:35:48 -0700},
	Date-Modified = {2020-08-21 23:47:21 -0700},
	Journal = {{Journal of Causal Inference}},
	Number = {2},
	Title = {Covariate Balancing Inverse Probability Weights for Time-Varying Continuous Interventions},
	Url = {https://doi.org/10.1515/jci-2017-0002},
	Volume = {6},
	Year = {2018},
	Bdsk-Url-1 = {https://www.degruyter.com/view/journals/jci/6/2/article-20170002.xml},
	Bdsk-Url-2 = {https://doi.org/10.1515/jci-2017-0002}}

@article{khuri_rsm,
	Abstract = {Abstract The purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951--1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976--1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright {\copyright} 2010 John Wiley \& Sons, Inc. This article is categorized under: Statistical Models > Linear Models Statistical Models > Nonlinear Models},
	Author = {Khuri, Andr{\'e} I. and Mukhopadhyay, Siuli},
	Date-Added = {2020-07-11 21:24:24 -0700},
	Date-Modified = {2020-07-11 21:27:55 -0700},
	Doi = {10.1002/wics.73},
	Journal = {WIREs Computational Statistics},
	Keywords = {graphical procedures, generalized linear models, multiresponse experiments, random effects, robust parameter designs},
	Number = {2},
	Pages = {128-149},
	Title = {Response surface methodology},
	Volume = {2},
	Year = {2010},
	Bdsk-Url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.73},
	Bdsk-Url-2 = {https://doi.org/10.1002/wics.73}}

@article{hypertension_combo_tx,
	Abstract = {The goal of antihypertensive therapy is to abolish the risks associated with blood pressure (BP) elevation without adversely affecting quality of life. Drug selection is based on efficacy in lowering BP and in reducing cardiovascular (CV) end points including stroke, myocardial infarction, and heart failure. Although the choice of initial drug therapy exerts some effect on long-term outcomes, it is evident that BP reduction per se is the primary determinant of CV risk reduction. Available data suggest that at least 75% of patients will require combination therapy to achieve contemporary BP targets, and increasing emphasis is being placed on the practical tasks involved in consistently achieving and maintaining goal BP in clinical practice. It is within this context that the American Society of Hypertension presents this Position Paper on Combination Therapy for Hypertension. It will address the scientific basis of combination therapy, present the pharmacologic rationale for choosing specific drug combinations, and review patient selection criteria for initial and secondary use. The advantages and disadvantages of single pill (fixed) drug combinations, and the implications of recent clinical trials involving specific combination strategies will also be discussed.},
	Author = {Alan H. Gradman and Jan N. Basile and Barry L. Carter and George L. Bakris},
	Date-Added = {2020-07-11 15:20:56 -0700},
	Date-Modified = {2020-08-21 23:42:53 -0700},
	Journal = {Journal of the American Society of Hypertension},
	Number = {2},
	Pages = {90-98},
	Title = {Combination therapy in hypertension},
	Volume = {4},
	Year = {2010},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1933171110000434},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.jash.2010.03.001}}

@article{cancer_combo_tx,
	Abstract = {Anti-angiogenic therapy was proposed in 1971 as a means to treat solid tumors and in 1976 as a method of cancer prevention. Here we propose that this form of therapy, judiciously applied, can normalize the tumor vasculature and improve the delivery of therapeutics.},
	Author = {Jain, Rakesh K.},
	Date-Added = {2020-07-11 15:15:06 -0700},
	Date-Modified = {2020-08-21 23:49:46 -0700},
	Journal = {Nature Medicine},
	Number = {9},
	Pages = {987-989},
	Title = {Normalizing tumor vasculature with anti-angiogenic therapy: {A} new paradigm for combination therapy},
	Volume = {7},
	Year = {2001},
	Bdsk-Url-1 = {https://doi.org/10.1038/nm0901-987}}

@article{crohns_combo_tx,
	Author = {Colombel, Jean Fr{\'e}d{\'e}ric and Sandborn, William J. and Reinisch, Walter and Mantzaris, Gerassimos J. and Kornbluth, Asher and Rachmilewitz, Daniel and Lichtiger, Simon and D'Haens, Geert and Diamond, Robert H. and Broussard, Delma L. and Tang, Kezhen L. and van der Woude, C. Janneke and Rutgeerts, Paul},
	Date-Added = {2020-07-11 15:12:34 -0700},
	Date-Modified = {2020-08-21 23:29:56 -0700},
	Journal = {New England Journal of Medicine},
	Number = {15},
	Pages = {1383-1395},
	Title = {Infliximab, Azathioprine, or Combination Therapy for {Crohn's} Disease},
	Volume = {362},
	Year = {2010},
	Bdsk-Url-1 = {https://doi.org/10.1056/NEJMoa0904492}}

@article{austin_gps_bal,
	Abstract = { Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups. Methods for balance assessment have been well described and are frequently implemented when using the propensity score with binary exposures. However, there is a paucity of information on how to assess baseline covariate balance when using the generalized propensity score. We describe how methods based on the standardized difference can be adapted for use with quantitative exposures when using the generalized propensity score. We also describe a method based on assessing the correlation between the quantitative exposure and each covariate in the sample when weighted using generalized propensity score -based weights. We conducted a series of Monte Carlo simulations to evaluate the performance of these methods. We also compared two different methods of estimating the generalized propensity score: ordinary least squared regression and the covariate balancing propensity score method. We illustrate the application of these methods using data on patients hospitalized with a heart attack with the quantitative exposure being creatinine level. },
	Author = {Peter C Austin},
	Date-Added = {2020-07-11 13:41:04 -0700},
	Date-Modified = {2020-07-11 13:41:36 -0700},
	Doi = {10.1177/0962280218756159},
	Journal = {Statistical Methods in Medical Research},
	Number = {5},
	Pages = {1365-1377},
	Title = {Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures},
	Volume = {28},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1177/0962280218756159}}

@book{kish_ess,
	Address = {New York},
	Author = {Leslie Kish},
	Date-Added = {2020-07-09 18:03:55 -0700},
	Date-Modified = {2020-07-09 18:07:14 -0700},
	Publisher = {John Wiley \& Sons},
	Title = {Survey Sampling},
	Year = {1965}}

@article{austin_iptw_best_practices,
	Abstract = {The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher-order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of `best practice' when using IPTW to estimate causal treatment effects using observational data. {\copyright} 2015 The Authors. Statistics in Medicine Published by John Wiley \& Sons Ltd.},
	Author = {Austin, Peter C. and Stuart, Elizabeth A.},
	Date-Added = {2020-07-09 10:22:58 -0700},
	Date-Modified = {2020-07-09 10:23:29 -0700},
	Doi = {10.1002/sim.6607},
	Journal = {Statistics in Medicine},
	Number = {28},
	Pages = {3661-3679},
	Title = {Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies},
	Volume = {34},
	Year = {2015},
	Bdsk-Url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.6607},
	Bdsk-Url-2 = {https://doi.org/10.1002/sim.6607}}

@article{Brown2020,
	Abstract = {Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from ``big data'' are lacking---the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.},
	Author = {Brown, Derek W. and DeSantis, Stacia M. and Greene, Thomas J. and Maroufy, Vahed and Yaseen, Ashraf and Wu, Hulin and Williams, George and Swartz, Michael D.},
	Date-Added = {2020-07-06 08:39:00 -0700},
	Date-Modified = {2020-07-06 08:39:00 -0700},
	Doi = {10.1002/sim.8540},
	Issn = {10970258},
	Journal = {Statistics in Medicine},
	Keywords = {causal inference,multinomial treatments,observational study,propensity score},
	Publisher = {John Wiley and Sons Ltd},
	Title = {{A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record--derived study}},
	Year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1002/sim.8540}}

@incollection{robins2000marginal,
	Address = {New York, NY},
	Author = {Robins, James M},
	Booktitle = {Statistical Models in Epidemiology: The Environment and Clinical Trials},
	Date-Added = {2020-07-01 14:45:48 -0700},
	Date-Modified = {2020-08-22 15:37:36 -0700},
	Editor = {M.E. Halloran and D. Berry},
	Pages = {95-134},
	Publisher = {Springer},
	Title = {Marginal structural models versus structural nested models as tools for causal inference},
	Year = {2000}}

@article{nobari2013immigrant,
	Author = {Nobari, Tabashir Z and Wang, May-Choo and Chaparro, M Pia and Crespi, Catherine M and Koleilat, Maria and Whaley, Shannon E},
	Date-Added = {2020-06-15 14:24:26 -0700},
	Date-Modified = {2020-06-15 14:24:53 -0700},
	Journal = {{Social Science \& Medicine}},
	Pages = {1-8},
	Publisher = {Elsevier},
	Title = {{Immigrant enclaves and obesity in preschool-aged children in Los Angeles County}},
	Volume = {92},
	Year = {2013}}

@article{hillier2012impact,
	Author = {Hillier, Amy and McLaughlin, Jacqueline and Cannuscio, Carolyn C and Chilton, Mariana and Krasny, Sarah and Karpyn, Allison},
	Date-Added = {2020-06-15 11:32:47 -0700},
	Date-Modified = {2020-06-15 11:35:33 -0700},
	Journal = {{Journal of Nutrition Education and Behavior}},
	Number = {3},
	Pages = {210-216},
	Publisher = {Elsevier},
	Title = {{The impact of WIC food package changes on access to healthful food in 2 low-income urban neighborhoods}},
	Volume = {44},
	Year = {2012}}

@misc{food_pacakage_change,
	Author = {{US Department of Agriculture, Food and Nutrition Service}},
	Date-Added = {2020-06-15 11:28:22 -0700},
	Date-Modified = {2020-08-22 16:34:54 -0700},
	Title = {Final Rule: {Revisions} in the {WIC} Food Packages},
	Url = {https://www.fns.usda.gov/wic/fr-030414},
	Year = {2014},
	Bdsk-Url-1 = {https://www.fns.usda.gov/wic/fr-030414}}

@misc{wic_data_mining,
	Author = {{PHFE WIC}},
	Date-Added = {2020-06-15 10:57:24 -0700},
	Date-Modified = {2020-08-22 20:33:25 -0700},
	Note = {{Last checked on August 22, 2020}},
	Title = {{L.A. County WIC Data}},
	Url = {https://lawicdata.org},
	Year = {2020},
	Bdsk-Url-1 = {https://apps.phfewic.org/Projects/DataMining.aspx}}

@misc{walls2013national,
	Author = {{Walls \& Associates}},
	Date-Added = {2020-06-14 21:03:08 -0700},
	Date-Modified = {2020-08-22 16:33:40 -0700},
	Note = {{Last checked on August 22, 2020}},
	Title = {{National Establishment Time-Series (NETS) database}},
	Url = {http://youreconomy.org/profile/about.lasso},
	Year = {2013},
	Bdsk-Url-1 = {http://youreconomy.org/profile/about.lasso}}

@article{barnatchez2017assessment,
	Author = {Barnatchez, Keith and Crane, Leland Dod and Decker, Ryan},
	Date-Added = {2020-06-14 20:58:31 -0700},
	Date-Modified = {2020-06-14 20:59:10 -0700},
	Publisher = {FEDS working paper},
	Title = {{An assessment of the National Establishment Time Series (NETs) database}},
	Year = {2017}}

@article{wang2018developing,
	Author = {Wang, May C and Crespi, Catherine M and Jiang, Linghui H and Nobari, Tabashir and Roper-Fingerhut, Hayley and Rauzon, Suzanne and Robles, Brenda and Blocklin, Michelle and Davoudi, Mehrnaz and Kuo, Tony and MacLeod, Kara E and Seto, Edmund and Whaley, Shannon and Prelip, Michael},
	Date-Added = {2020-06-13 19:52:51 -0700},
	Date-Modified = {2020-08-22 15:53:43 -0700},
	Journal = {Preventive Medicine},
	Pages = {135-141},
	Title = {Developing an index of dose of exposure to early childhood obesity community interventions},
	Volume = {111},
	Year = {2018}}

@article{kang2007demystifying,
	Author = {Kang, Joseph DY and Schafer, Joseph L},
	Date-Added = {2020-06-12 14:56:24 -0700},
	Date-Modified = {2020-08-21 23:51:34 -0700},
	Journal = {Statistical Science},
	Number = {4},
	Pages = {523-539},
	Title = {Demystifying double robustness: {A} comparison of alternative strategies for estimating a population mean from incomplete data},
	Volume = {22},
	Year = {2007}}

@article{diamond2013genetic,
	Author = {Diamond, Alexis and Sekhon, Jasjeet S},
	Date-Added = {2020-06-12 13:53:08 -0700},
	Date-Modified = {2020-08-21 23:33:13 -0700},
	Journal = {Review of Economics and Statistics},
	Number = {3},
	Pages = {932-945},
	Title = {Genetic matching for estimating causal effects: {A} general multivariate matching method for achieving balance in observational studies},
	Volume = {95},
	Year = {2013}}

@article{crump2009dealing,
	Author = {Crump, Richard K and Hotz, V Joseph and Imbens, Guido W and Mitnik, Oscar A},
	Date-Added = {2020-06-12 13:47:09 -0700},
	Date-Modified = {2020-06-12 13:47:16 -0700},
	Journal = {Biometrika},
	Number = {1},
	Pages = {187-199},
	Publisher = {Oxford University Press},
	Title = {Dealing with limited overlap in estimation of average treatment effects},
	Volume = {96},
	Year = {2009}}

@manual{R_ref,
	Address = {Vienna, Austria},
	Author = {{R Core Team}},
	Date-Added = {2020-06-08 12:04:02 -0700},
	Date-Modified = {2020-06-08 12:04:15 -0700},
	Organization = {R Foundation for Statistical Computing},
	Title = {{R: A Language and Environment for Statistical Computing}},
	Url = {https://www.R-project.org/},
	Year = {2020},
	Bdsk-Url-1 = {https://www.R-project.org/}}

@manual{weightIt,
	Author = {Noah Greifer},
	Date-Added = {2020-06-08 12:01:58 -0700},
	Date-Modified = {2020-08-21 23:44:09 -0700},
	Note = {{R package version 0.9.0}},
	Title = {{WeightIt: Weighting for Covariate Balance in Observational Studies}},
	Url = {https://CRAN.R-project.org/package=WeightIt},
	Year = {2020},
	Bdsk-Url-1 = {https://CRAN.R-project.org/package=WeightIt}}

@article{chaparro2014influences,
	Author = {Chaparro, M Pia and Whaley, Shannon E and Crespi, Catherine M and Koleilat, Maria and Nobari, Tabashir Z and Seto, Edmund and Wang, May C},
	Date-Added = {2020-05-17 09:58:58 -0700},
	Date-Modified = {2020-08-21 23:34:53 -0700},
	Journal = {J Epidemiol Community Health},
	Number = {11},
	Pages = {1027-1033},
	Title = {Influences of the neighbourhood food environment on adiposity of low-income preschool-aged children in {Los Angeles County}: {A} longitudinal study},
	Volume = {68},
	Year = {2014}}

@article{huber2013performance,
	Author = {Huber, Martin and Lechner, Michael and Wunsch, Conny},
	Date-Added = {2020-05-15 19:49:15 -0700},
	Date-Modified = {2020-05-15 19:49:27 -0700},
	Journal = {{Journal of Econometrics}},
	Number = {1},
	Pages = {1-21},
	Publisher = {Elsevier},
	Title = {The performance of estimators based on the propensity score},
	Volume = {175},
	Year = {2013}}

@article{lee2011weight,
	Author = {Lee, Brian K and Lessler, Justin and Stuart, Elizabeth A},
	Date-Added = {2020-05-15 19:46:53 -0700},
	Date-Modified = {2020-08-21 23:55:01 -0700},
	Journal = {PLOS ONE},
	Number = {3},
	Title = {Weight trimming and propensity score weighting},
	Url = {https://doi.org/10.1371/journal.pone.0018174},
	Volume = {6},
	Year = {2011},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pone.0018174}}

@misc{kallus2019kernel,
	Archiveprefix = {arXiv},
	Author = {Nathan Kallus and Michele Santacatterina},
	Date-Added = {2020-04-02 13:26:25 -0700},
	Date-Modified = {2020-04-02 13:26:25 -0700},
	Eprint = {1910.11972},
	Primaryclass = {stat.ME},
	Title = {Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments},
	Year = {2019}}

@article{kennedy2017,
	Abstract = {Summary Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and give a procedure for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.},
	Author = {Kennedy, Edward H. and Ma, Zongming and McHugh, Matthew D. and Small, Dylan S.},
	Date-Added = {2020-04-02 13:26:25 -0700},
	Date-Modified = {2020-04-08 20:03:46 -0700},
	Journal = {{Journal of the Royal Statistical Society: Series B}},
	Number = {4},
	Pages = {1229-1245},
	Title = {Non-parametric methods for doubly robust estimation of continuous treatment effects},
	Volume = {79},
	Year = {2017},
	Bdsk-Url-1 = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12212},
	Bdsk-Url-2 = {https://doi.org/10.1111/rssb.12212}}

@book{lewis1973counterfactuals,
	Author = {Lewis, David},
	City = {Oxford},
	Date-Added = {2020-04-02 13:26:25 -0700},
	Date-Modified = {2020-04-02 13:26:25 -0700},
	Publisher = {Blackwell},
	Title = {Counterfactuals},
	Year = {1973}}

@article{lewis1974causation,
	Author = {Lewis, David},
	Date-Added = {2020-04-02 13:26:25 -0700},
	Date-Modified = {2020-04-02 13:26:25 -0700},
	Journal = {{The Journal of Philosophy}},
	Number = {17},
	Pages = {556-567},
	Title = {Causation},
	Volume = {70},
	Year = {1974}}

@article{tbbicke2020entropy,
	Archiveprefix = {{arXiv}},
	Author = {Stefan T{\"u}bbicke},
	Date-Added = {2020-03-12 12:44:37 -0700},
	Date-Modified = {2020-08-22 15:47:30 -0700},
	Eprint = {{2001.06281}},
	Journal = {{arXiv preprint arXiv:2001.06281}},
	Primaryclass = {econ.EM},
	Title = {Entropy Balancing for Continuous Treatments},
	Year = {2020}}

@article{vegetabile2020nonparametric,
	Archiveprefix = {{arXiv}},
	Author = {Brian G. Vegetabile and Beth Ann Griffin and Donna L. Coffman and Matthew Cefalu and Daniel F. McCaffrey},
	Date-Added = {2020-03-12 12:34:56 -0700},
	Date-Modified = {2020-08-22 15:48:26 -0700},
	Eprint = {2003.02938},
	Journal = {{arXiv preprint arXiv:2003.02938}},
	Primaryclass = {stat.ME},
	Title = {Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures},
	Year = {2020}}

@article{papadogeorgou2018adjusting,
	Author = {Papadogeorgou, Georgia and Choirat, Christine and Zigler, Corwin M},
	Date-Added = {2019-05-16 08:54:49 -0700},
	Date-Modified = {2019-05-16 08:54:49 -0700},
	Journal = {Biostatistics},
	Number = {2},
	Pages = {256--272},
	Publisher = {Oxford University Press},
	Title = {Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching},
	Volume = {20},
	Year = {2018}}

@article{papadogeorgou2019causal,
	Author = {Papadogeorgou, Georgia and Mealli, Fabrizia and Zigler, Corwin M},
	Date-Added = {2019-05-16 08:53:38 -0700},
	Date-Modified = {2019-05-16 08:53:38 -0700},
	Journal = {Biometrics},
	Publisher = {Wiley Online Library},
	Title = {Causal Inference With Interfering Units for Cluster and Population Level Treatment Allocation Programs},
	Year = {2019}}

@article{forastiere2016identification,
	Author = {Forastiere, Laura and Airoldi, Edoardo M and Mealli, Fabrizia},
	Date-Added = {2019-05-15 15:27:27 -0700},
	Date-Modified = {2019-05-15 15:27:27 -0700},
	Journal = {arXiv preprint arXiv:1609.06245},
	Title = {Identification and estimation of treatment and interference effects in observational studies on networks},
	Year = {2016}}

@article{verbitsky2012causal,
	Author = {Verbitsky-Savitz, Natalya and Raudenbush, Stephen W},
	Date-Added = {2019-05-15 15:23:46 -0700},
	Date-Modified = {2019-05-15 15:23:46 -0700},
	Journal = {Epidemiologic Methods},
	Number = {1},
	Pages = {107--130},
	Publisher = {De Gruyter},
	Title = {Causal inference under interference in spatial settings: A case study evaluating community policing program in Chicago},
	Volume = {1},
	Year = {2012}}

@article{nickerson2008voting,
	Annote = {url: https://www.jstor.org/stable/pdf/27644497.pdf},
	Author = {Nickerson, David W},
	Date-Added = {2019-04-22 15:48:04 -0700},
	Date-Modified = {2019-04-22 15:49:35 -0700},
	Journal = {{American Political Science Review}},
	Number = {1},
	Pages = {49-57},
	Publisher = {Cambridge University Press},
	Title = {{Is voting contagious? Evidence from two field experiments}},
	Volume = {102},
	Year = {2008}}

@article{halloran1995causal,
	Annote = {url: https://www.jstor.org/stable/3702315?seq=1#metadata_info_tab_contents},
	Author = {Halloran, M Elizabeth and Struchiner, Claudio J},
	Date-Added = {2019-04-22 15:45:13 -0700},
	Date-Modified = {2019-04-22 15:47:00 -0700},
	Journal = {{Epidemiology}},
	Number = {2},
	Pages = {142-151},
	Read = {0},
	Title = {Causal inference in infectious diseases},
	Volume = {6},
	Year = {1995}}

@article{liu2016inverse,
	Author = {Liu, Lan and Hudgens, Michael G and Becker-Dreps, Sylvia},
	Date-Added = {2019-04-22 15:43:43 -0700},
	Date-Modified = {2019-04-22 15:43:53 -0700},
	Journal = {Biometrika},
	Number = {4},
	Pages = {829-842},
	Publisher = {Oxford University Press},
	Title = {On inverse probability-weighted estimators in the presence of interference},
	Volume = {103},
	Year = {2016}}

@article{sinclair2012detecting,
	Author = {Sinclair, Betsy and McConnell, Margaret and Green, Donald P},
	Date-Added = {2019-04-22 15:41:41 -0700},
	Date-Modified = {2019-04-22 15:43:43 -0700},
	Journal = {{American Journal of Political Science}},
	Number = {4},
	Pages = {1055-1069},
	Publisher = {Wiley Online Library},
	Title = {Detecting spillover effects: Design and analysis of multilevel experiments},
	Volume = {56},
	Year = {2012}}

@article{tchetgen2012causal,
	Author = {Tchetgen Tchetgen, Eric J and VanderWeele, Tyler J},
	Date-Added = {2019-04-22 15:38:48 -0700},
	Date-Modified = {2019-04-22 15:39:40 -0700},
	Journal = {{Statistical Methods in Medical Research}},
	Number = {1},
	Pages = {55-75},
	Publisher = {Sage Publications Sage UK: London, England},
	Title = {On causal inference in the presence of interference},
	Volume = {21},
	Year = {2012}}

@article{arpino2011,
	Author = {Arpino, Bruno and Mealli, Fabrizia},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Computational Statistics \& Data Analysis},
	Number = {4},
	Pages = {1770-1780},
	Publisher = {Elsevier},
	Title = {The specification of the propensity score in multilevel observational studies},
	Volume = {55},
	Year = {2011}}

@book{banerjee,
	Address = {New York},
	Author = {Banerjee, Sudipto and Carlin, Bradley and Gelfand, Alan},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Edition = {2nd},
	Publisher = {Chapman \& Hall},
	Title = {Hierarchical Modeling and Analysis for Spatial Data},
	Year = {2015}}

@article{berry2017,
	Author = {Berry, Daniel and Blair, Clancy and Willoughby, Michael and Granger, Douglas A. and Mills-Koonce, W. Roger},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Development and Psychopathology},
	Number = {1},
	Pages = {303-317},
	Publisher = {Cambridge University Press},
	Title = {Maternal sensitivity and adrenocortical functioning across infancy and toddlerhood: Physiological adaptation to context?},
	Volume = {29},
	Year = {2017},
	Bdsk-Url-1 = {https://dx.doi.org/10.1017/S0954579416000158}}

@article{bia2012assessing,
	Author = {Bia, Michela and Mattei, Alessandra},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistical Methods \& Applications},
	Pages = {1-32},
	Publisher = {Springer},
	Title = {Assessing the effect of the amount of financial aids to {P}iedmont firms using the generalized propensity score},
	Year = {2012}}

@article{chagas2012,
	Abstract = { The expansion of sugarcane growing in Brazil, spurred particularly by increased demand for ethanol, has triggered the need to evaluate the economic, social, and environmental impacts of this process, both on the country as a whole and on the growing regions. Even though the balance of costs and benefits is positive from an overall standpoint, this may not be so in specific producing regions, due to negative externalities. The objective of this paper is to estimate the effect of growing sugarcane on the human development index (HDI) and its sub-indices in cane producing regions. In the literature on matching effects, this is interpreted as the effect of the treatment on the treated. Location effects are controlled by spatial econometric techniques, giving rise to the spatial propensity score matching model. The authors analyze 424 minimum comparable areas (MCAs) in the treatment group, compared with 907 MCAs in the control group. The results suggest that the presence of sugarcane growing in these areas is not relevant to determine their social conditions, whether for better or worse. It is thus likely that public policies, especially those focused directly on improving education, health, and income generation/distribution, have much more noticeable effects on the municipal HDI. },
	Author = {Andr{\'e} Luis Squarize Chagas and Rudinei Toneto and Carlos Roberto Azzoni},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {International Regional Science Review},
	Number = {1},
	Pages = {48-69},
	Title = {A Spatial Propensity Score Matching Evaluation of the Social Impacts of Sugarcane Growing on Municipalities in Brazil},
	Volume = {35},
	Year = {2012},
	Bdsk-Url-1 = {https://doi.org/10.1177/0160017611400069}}

@article{cochran1973,
	Author = {Cochran, William G and Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Sankhy{\=a}: The Indian Journal of Statistics, Series A},
	Number = {4},
	Pages = {417-446},
	Publisher = {JSTOR},
	Title = {Controlling bias in observational studies: a review},
	Volume = {35},
	Year = {1973}}

@article{cragg1971,
	Author = {John G. Cragg},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Econometrica},
	Number = {5},
	Pages = {829-844},
	Title = {Some statistical models for limited dependent variables with application to the demand for durable goods},
	Volume = {39},
	Year = {1971}}

@article{crespi2012validity,
	Author = {Crespi, Catherine M and Alfonso, Vivian H and Whaley, Shannon E and Wang, May C},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Pediatric Research},
	Number = {3},
	Pages = {286-292},
	Publisher = {NIH Public Access},
	Title = {Validity of child anthropometric measurements in the {Special Supplemental Nutrition Program for Women, Infants and Children}},
	Volume = {71},
	Year = {2012}}

@article{davis2017,
	Author = {Melanie L Davis and Brian Neelon and Paul J Nietert and Kelly J Hunt and Lane F. Burgette and Andrew B Lawson and Leonard E Egede},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Doi = {10.1177/0962280217735700},
	Eprint = {https://doi.org/10.1177/0962280217735700},
	Journal = {Statistical Methods in Medical Research},
	Note = {PMID: 29145767},
	Title = {Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes},
	Url = {https://doi.org/10.1177/0962280217735700},
	Bdsk-Url-1 = {https://doi.org/10.1177/0962280217735700}}

@techreport{decastris2015,
	Author = {De Castris, Marusca and Pellegrini, Guido and others},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Institution = {Centro di Ricerca Interdipartimentale di Economia delle Istituzioni (CREI)},
	Number = {5},
	Title = {Neighborhood effects on the propensity score matching},
	Type = {Working Paper},
	Year = {2015}}

@article{dehejia2002,
	Author = {Dehejia, Rajeev H and Wahba, Sadek},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Review of Economics and Statistics},
	Number = {1},
	Pages = {151-161},
	Publisher = {MIT Press},
	Title = {Propensity score-matching methods for nonexperimental causal studies},
	Volume = {84},
	Year = {2002}}

@article{dempster1977,
	Author = {Dempster, A.P. and Laird, N.M. and Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the Royal Statistical Society: Series B},
	Number = {1},
	Pages = {1-38},
	Title = {Maximum likelihood from incomplete data via the {EM} algorithm},
	Volume = {39},
	Year = {1977}}

@article{doyle2011,
	Author = {Doyle, William R},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Economics of Education Review},
	Number = {1},
	Pages = {191-200},
	Publisher = {Elsevier},
	Title = {Effect of increased academic momentum on transfer rates: an application of the generalized propensity score},
	Volume = {30},
	Year = {2011}}

@article{duan1983,
	Author = {Duan, Naihua and Manning, Willard G and Morris, Carl N and Newhouse, Joseph P},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of Business \& Economic Statistics},
	Number = {2},
	Pages = {115-126},
	Publisher = {Taylor \& Francis Group},
	Title = {A comparison of alternative models for the demand for medical care},
	Volume = {1},
	Year = {1983}}

@article{eckardt2012,
	Author = {Eckardt, Patricia},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Nursing Research},
	Number = {3},
	Pages = {213-223},
	Publisher = {LWW},
	Title = {Propensity score estimates in multilevel models for causal inference},
	Volume = {61},
	Year = {2012}}

@article{flores2012,
	Author = {Flores, Carlos A and Flores-Lagunes, Alfonso and Gonzalez, Arturo and Neumann, Todd C},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 23:33:37 -0700},
	Journal = {Review of Economics and Statistics},
	Number = {1},
	Pages = {153-171},
	Title = {Estimating the effects of length of exposure to instruction in a training program: {The} case of job corps},
	Volume = {94},
	Year = {2012}}

@article{fong2018,
	Author = {Fong, Christian and Hazlett, Chad and Imai, Kosuke},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 23:37:16 -0700},
	Journal = {Annals of Applied Statistics},
	Number = {1},
	Pages = {156-177},
	Title = {Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements},
	Volume = {12},
	Year = {2018}}

@article{green2017,
	Author = {Green, Mark A and Arcaya, Mariana and Subramanian, SV},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Annals of the American Association of Geographers},
	Pages = {1--13},
	Publisher = {Taylor \& Francis},
	Title = {Using Internal Migration to Estimate the Causal Effect of Neighborhood Socioeconomic Context on Health: A Longitudinal Analysis, England, 1995--2008},
	Year = {2017}}

@book{guo_propensity,
	Address = {Los Angeles},
	Author = {Guo, Shenyang And Fraser, Mark W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Edition = {2nd},
	Publisher = {Sage},
	Title = {Propensity Score Analysis: Statistical Methods and Applications},
	Year = {2014}}

@article{hade2014,
	Author = {Hade, Erinn M and Lu, Bo},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistics in Medicine},
	Number = {1},
	Pages = {74-87},
	Publisher = {Wiley Online Library},
	Title = {Bias associated with using the estimated propensity score as a regression covariate},
	Volume = {33},
	Year = {2014}}

@article{hirano2001,
	Author = {Hirano, Keisuke and Imbens, Guido W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Health Services and Outcomes Research Methodology},
	Pages = {259-278},
	Publisher = {Springer},
	Title = {Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization},
	Volume = {2},
	Year = {2001}}

@article{hirano2003,
	Author = {Hirano, Keisuke and Imbens, Guido W and Ridder, Geert},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Econometrica},
	Number = {4},
	Pages = {1161-1189},
	Publisher = {Wiley Online Library},
	Title = {Efficient estimation of average treatment effects using the estimated propensity score},
	Volume = {71},
	Year = {2003}}

@incollection{hirano_continuous,
	Address = {Hoboken, N.J.},
	Author = {Hirano, Keisuke and Imbens, Guido W},
	Booktitle = {Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 22:11:23 -0700},
	Editor = {Andrew Gelman and Xiao-Li Meng},
	Pages = {73-84},
	Publisher = {John Wiley \& Sons},
	Title = {The propensity score with continuous treatments},
	Year = {2004}}

@article{holland1986,
	Abstract = {Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.},
	Author = {Paul W. Holland},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {396},
	Pages = {945-960},
	Title = {Statistics and Causal Inference},
	Volume = {81},
	Year = {1986},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2289064}}

@article{hong2006,
	Author = {Hong, Guanglei and Raudenbush, Stephen W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {475},
	Pages = {901-910},
	Publisher = {Taylor \& Francis},
	Title = {Evaluating kindergarten retention policy: a case study of causal inference for multilevel observational data},
	Volume = {101},
	Year = {2006}}

@article{horvitz1952,
	Author = {Horvitz, Daniel G and Thompson, Donovan J},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {260},
	Pages = {663-685},
	Publisher = {Taylor \& Francis Group},
	Title = {A generalization of sampling without replacement from a finite universe},
	Volume = {47},
	Year = {1952}}

@article{imai_cbps,
	Author = {Imai, Kosuke and Ratkovic, Marc},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the Royal Statistical Society: Series B},
	Number = {1},
	Pages = {243-263},
	Publisher = {Wiley Online Library},
	Title = {Covariate balancing propensity score},
	Volume = {76},
	Year = {2014}}

@article{imai_causalGPS,
	Author = {Imai, Kosuke and Van Dyk, David A},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 23:47:55 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {467},
	Pages = {854-866},
	Title = {Causal inference with general treatment regimes: {Generalizing} the propensity score},
	Volume = {99},
	Year = {2004}}

@article{imbens_dose_resp,
	Author = {Imbens, Guido W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 22:01:34 -0700},
	Journal = {Biometrika},
	Number = {3},
	Pages = {706-710},
	Title = {The role of the propensity score in estimating dose-response functions},
	Volume = {87},
	Year = {2000}}

@article{jiang2013,
	Author = {Jiang, Miao and Foster, E. Michael},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Health Services Research},
	Number = {2.1},
	Pages = {628-651},
	Title = {Duration of breastfeeding and childhood obesity: a generalized propensity score approach},
	Volume = {48},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1111/j.1475-6773.2012.01456.x}}

@article{jones1989,
	Author = {Jones, Andrew M.},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of Applied Econometrics},
	Number = {1},
	Pages = {23-39},
	Title = {A double-hurdle model of cigarette consumption},
	Volume = {4},
	Year = {1989},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/jae.3950040103}}

@article{kelcey2011stratification,
	Abstract = { This study investigated the relationship of teachers' reading knowledge with students' reading achievement using a direct teacher knowledge assessment rather than indirect proxies (e.g., certification). To address the inequitable distribution of teachers' knowledge resulting from differences in teachers' backgrounds and the disparities in how schools attract and cultivate knowledge, the study developed multilevel propensity score methods to identify comparable teachers on the basis of both teacher and school backgrounds. Results suggest that schools are complexly associated with differences in teachers' knowledge and that comparisons which ignore the relevance of schools may be misleading. By comparing teachers with similar personal and school backgrounds, results show measured knowledge is significantly associated with students' achievement in reading comprehension but not word analysis. The findings support policies which leverage school capacities to develop the specialized knowledge needed for teaching reading. },
	Author = {Ben Kelcey},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Educational Evaluation and Policy Analysis},
	Number = {4},
	Pages = {458-482},
	Title = {Assessing the Effects of Teachers' Reading Knowledge on Students' Achievement Using Multilevel Propensity Score Stratification},
	Volume = {33},
	Year = {2011},
	Bdsk-Url-1 = {https://doi.org/10.3102/0162373711415262}}

@article{kelcey2011,
	Author = {Ben Kelcey},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Multivariate Behavioral Research},
	Number = {3},
	Pages = {453-476},
	Title = {Covariate selection in propensity scores using outcome proxies},
	Volume = {46},
	Year = {2011},
	Bdsk-Url-1 = {https://doi.org/10.1080/00273171.2011.570164}}

@phdthesis{kelcey2009,
	Author = {Kelcey, Benjamin M},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	School = {University of Michigan},
	Title = {Improving and assessing propensity score based causal inferences in multilevel and nonlinear settings},
	Year = {2009}}

@inproceedings{kim_hierarchalpropensity,
	Address = {Cham},
	Author = {Kim, Jee-Seon and Steiner, Peter M},
	Booktitle = {Quantitative Psychology Research},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Editor = {van der Ark, L. Andreis and Bolt, Daniel M. and Wang, Wen-Chung and Douglas, Jeffrey A. and Wiberg, Marie},
	Pages = {293-306},
	Publisher = {Springer International Publishing},
	Title = {Multilevel propensity score methods for estimating causal effects: a latent class modeling strategy},
	Year = {2015}}

@techreport{kim2007,
	Address = {Los Angeles},
	Author = {Kim, Junyeop and Seltzer, Michael},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Institution = {CRESST/University of California},
	Month = {February},
	Number = {708},
	Title = {Causal inference in multilevel settings in which selection processes vary across schools},
	Type = {CSE Technical Report},
	Year = {2007}}

@article{kluve_gps,
	Author = {Kluve, Jochen and Schneider, Hilmar and Uhlendorff, Arne and Zhao, Zhong},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the Royal Statistical Society: Series A},
	Number = {2},
	Pages = {587-617},
	Publisher = {Wiley Online Library},
	Title = {Evaluating continuous training programmes by using the generalized propensity score},
	Volume = {175},
	Year = {2012}}

@article{kreif_gps_ml,
	Author = {Kreif, No{\'e}mi and Grieve, Richard and D{\'\i}az, Iv{\'a}n and Harrison, David},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 23:52:51 -0700},
	Journal = {Health Economics},
	Number = {9},
	Pages = {1213-1228},
	Title = {Evaluation of the effect of a continuous treatment: {A} machine learning approach with an application to treatment for traumatic brain injury},
	Volume = {24},
	Year = {2015}}

@article{lechner2001,
	Author = {Lechner, Michael},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Econometric Evaluation of Labour Market Policies},
	Pages = {43-58},
	Publisher = {Springer},
	Title = {Identification and estimation of causal effects of multiple treatments under the conditional independence assumption},
	Year = {2001}}

@article{leite2015,
	Author = {Leite, Walter L and Jimenez, Francisco and Kaya, Yasemin and Stapleton, Laura M and MacInnes, Jann W and Sandbach, Robert},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Multivariate Behavioral Research},
	Number = {3},
	Pages = {265-284},
	Publisher = {Taylor \& Francis},
	Title = {An evaluation of weighting methods based on propensity scores to reduce selection bias in multilevel observational studies},
	Volume = {50},
	Year = {2015}}

@article{li2013,
	Author = {Li, Fan and Zaslavsky, Alan M and Landrum, Mary Beth},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistics in Medicine},
	Number = {19},
	Pages = {3373-3387},
	Publisher = {Wiley Online Library},
	Title = {Propensity score weighting with multilevel data},
	Volume = {32},
	Year = {2013}}

@book{little2014,
	Address = {New York},
	Author = {Little, Roderick JA and Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Edition = {2nd},
	Publisher = {John Wiley \& Sons},
	Title = {Statistical Analysis with Missing Data},
	Year = {2014}}

@article{mccormick2013,
	Author = {Meghan P. McCormick and Erin E. O'Connor and Elise Cappella and Sandee G. McClowry},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of School Psychology},
	Number = {5},
	Pages = {611-624},
	Title = {Teacher-child relationships and academic achievement: a multilevel propensity score model approach},
	Volume = {51},
	Year = {2013},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0022440513000393},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.jsp.2013.05.001}}

@incollection{mcfadden1973,
	Address = {Cambridge},
	Author = {Daniel McFadden},
	Booktitle = {Frontiers in Econometrics},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Editor = {Paul Zarembka},
	Pages = {105-142},
	Publisher = {Academic Press},
	Title = {Conditional Logit Analysis of Qualitative Choice Behavior},
	Year = {1973}}

@inproceedings{mcfadden1978,
	Address = {Amsterdam},
	Author = {Daniel McFadden},
	Booktitle = {Spatial Interaction Theory and Planning Models},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Editor = {Karlqvist, A and Lundqvist, L and Snickars, F and Weibull, J},
	Pages = {72-77},
	Publisher = {North-Holland},
	Title = {Modelling the Choice of Residential Locations},
	Year = {1978}}

@article{min2002,
	Author = {Min, Y. and Agresti, A.},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the Iranian Statistical Society},
	Number = {1-2},
	Pages = {7-33},
	Title = {Modeling nonnegative data with clumping at zero: a survey},
	Volume = {1},
	Year = {2002}}

@article{moodie_logitudinal,
	Author = {Moodie, Erica EM and Stephens, David A},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistical Methods in Medical Research},
	Number = {2},
	Pages = {149-166},
	Publisher = {Sage Publications Sage UK: London, England},
	Title = {Estimation of dose-response functions for longitudinal data using the generalised propensity score},
	Volume = {21},
	Year = {2012}}

@article{neyman1923,
	Author = {Neyman, J.},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Roczniki Nauk Rolniczych Tom X {\normalfont [in Polish]; translated in} Statistical Science},
	Pages = {465-480},
	Title = {{On the application of probability theory to agricultural experiments. Essay on principles. Section 9}},
	Volume = {5},
	Year = {1923}}

@article{oakes2004,
	Author = {Oakes, J Michael},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Social Science \& Medicine},
	Number = {10},
	Pages = {1929-1952},
	Publisher = {Elsevier},
	Title = {The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology},
	Volume = {58},
	Year = {2004}}

@techreport{phfewic,
	Author = {{PHFE WIC}},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-22 15:30:23 -0700},
	Institution = {Special Supplemental Nutrition Assistance Program for Women, Infants and Children},
	Title = {{WIC} data 2003-2009: {A report on low income families with young children in Los Angeles County}},
	Type = {Technical Report},
	Year = {2010}}

@book{raudenbush_hlm,
	Address = {Thousand Oaks},
	Author = {Raudenbush, Stephen W and Bryk, Anthony S},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Publisher = {Sage},
	Title = {Hierarchical Linear Models: Applications and Data Analysis Methods},
	Year = {2002}}

@article{ricker2011,
	Author = {Ricker-Gilbert, Jacob and Jayne, Thomas S. and Chirwa, Ephraim},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {American Journal of Agricultural Economics},
	Number = {1},
	Pages = {26-42},
	Title = {Subsidies and Crowding Out: a Double-Hurdle Model of Fertilizer Demand in {Malawi}},
	Volume = {93},
	Year = {2011},
	Bdsk-Url-1 = {+%20http://dx.doi.org/10.1093/ajae/aaq122},
	Bdsk-Url-2 = {https://dx.doi.org/10.1093/ajae/aaq122}}

@article{robins2000,
	Author = {Robins, James M and Hernan, Miguel Angel and Brumback, Babette},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Epidemiology},
	Number = {5},
	Pages = {550-560},
	Publisher = {LWW},
	Title = {Marginal structural models and causal inference in epidemiology},
	Volume = {11},
	Year = {2000}}

@article{rosenbaum1987,
	Author = {Rosenbaum, Paul R},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {398},
	Pages = {387-394},
	Publisher = {Taylor \& Francis},
	Title = {Model-based direct adjustment},
	Volume = {82},
	Year = {1987}}

@article{rosenbaum1984,
	Author = {Rosenbaum, Paul R and Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {387},
	Pages = {516-524},
	Publisher = {Taylor \& Francis Group},
	Title = {Reducing bias in observational studies using subclassification on the propensity score},
	Volume = {79},
	Year = {1984}}

@article{rosenbaum_propensity,
	Author = {Rosenbaum, Paul R and Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-21 22:01:27 -0700},
	Journal = {Biometrika},
	Number = {1},
	Pages = {41-55},
	Title = {The central role of the propensity score in observational studies for causal effects},
	Volume = {70},
	Year = {1983}}

@article{rubin1976,
	Author = {Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Biometrika},
	Number = {3},
	Pages = {581-592},
	Publisher = {Oxford University Press},
	Title = {Inference and missing data},
	Volume = {63},
	Year = {1976}}

@article{rubin1974,
	Author = {Rubin, Donald B},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of Educational Psychology},
	Number = {5},
	Pages = {688-701},
	Publisher = {American Psychological Association},
	Title = {Estimating causal effects of treatments in randomized and nonrandomized studies},
	Volume = {66},
	Year = {1974}}

@article{rubin1986,
	Author = {Donald B. Rubin},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of the American Statistical Association},
	Pages = {961-962},
	Title = {Comment: which ifs have causal answers?},
	Volume = {81},
	Year = {1986}}

@article{rubin1980,
	Author = {Donald B. Rubin},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-22 15:39:14 -0700},
	Journal = {Journal of the American Statistical Association},
	Number = {371},
	Pages = {591-593},
	Publisher = {American Statistical Association, Taylor \& Francis, Ltd.},
	Title = {Randomization analysis of experimental data: {The Fisher} randomization test comment},
	Volume = {75},
	Year = {1980},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2287653}}

@article{savitz2009,
	Author = {Savitz, Natalya Verbitsky and Raudenbush, Stephen W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Sociological Methodology},
	Number = {1},
	Pages = {151-183},
	Publisher = {Wiley Online Library},
	Title = {Exploiting spatial dependence to improve measure ment of neighborhood social processes},
	Volume = {39},
	Year = {2009}}

@article{schuler2016,
	Author = {Schuler, Megan S and Chu, Wanghuan and Coffman, Donna},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Health Services and Outcomes Research Methodology},
	Number = {4},
	Pages = {271-292},
	Publisher = {Springer},
	Title = {Propensity score weighting for a continuous exposure with multilevel data},
	Volume = {16},
	Year = {2016}}

@article{spertus2018,
	Author = {Spertus, Jacob and Horvitz-Lennon, Marcela and Normand, Sharon-Lise},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {{[preprint arXiv:1802.05186]}},
	Title = {Bayesian Meta-Analysis of Multiple Continuous Treatments: an Application to Antipsychotic Drugs},
	Year = {2018}}

@inproceedings{steiner_matchinghierarchy,
	Address = {Alexandria, VA},
	Author = {Steiner, Peter M and Kim, Jee-Seon and Thoemmes, Felix},
	Booktitle = {\normalfont{Proceedings at the Joint Statistical Meetings}},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Pages = {5020-5032},
	Title = {Matching strategies for observational multilevel data},
	Year = {2013}}

@article{stuart2007,
	Abstract = { Education researchers, practitioners, and policymakers alike are committed to identifying interventions that teach students more effectively. Increased emphasis on evaluation and accountability has increased desire for sound evaluations of these interventions; and at the same time, school-level data have become increasingly available. This article shows researchers how to bridge these two trends through careful use of school-level data to estimate the effectiveness of particular interventions. The author provides an overview of common methods for estimating causal effects with school-level data, including randomized experiments, regression analysis, pre--post studies, and nonexperimental comparison group designs. She stresses the importance of careful design of nonexperimental studies, particularly the need to compare units that were similar before treatment assignment. She gives examples of analyses that use school-level data and concludes with advice for researchers. },
	Author = {Elizabeth A. Stuart},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Educational Researcher},
	Number = {4},
	Pages = {187-198},
	Title = {Estimating causal effects using school-level data sets},
	Volume = {36},
	Year = {2007},
	Bdsk-Url-1 = {https://doi.org/10.3102/0013189X07303396}}

@inproceedings{su2009,
	Address = {Toronto, Canada},
	Author = {Su, Yu-Sung and Cortina, Jeronimo},
	Booktitle = {\normalfont{Proccedings at American Political Science Association Meetings}},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Pages = {1-32},
	Title = {What do we gain? Combining propensity score methods and multilevel modeling},
	Year = {2009}}

@article{thoemmes_clusterPS,
	Author = {Thoemmes, Felix J and West, Stephen G},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Multivariate Behavioral Research},
	Number = {3},
	Pages = {514-543},
	Publisher = {Taylor \& Francis},
	Title = {The use of propensity scores for nonrandomized designs with clustered data},
	Volume = {46},
	Year = {2011}}

@article{thoemmes_psreview,
	Author = {Thoemmes, Felix J and Kim, Eun Sook},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Multivariate behavioral research},
	Number = {1},
	Pages = {90-118},
	Publisher = {Taylor \& Francis},
	Title = {A systematic review of propensity score methods in the social sciences},
	Volume = {46},
	Year = {2011}}

@article{tobin1958,
	Author = {Tobin, James},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Econometrica},
	Number = {1},
	Pages = {24-36},
	Publisher = {JSTOR},
	Title = {Estimation of relationships for limited dependent variables},
	Volume = {26},
	Year = {1958}}

@misc{census,
	Author = {{United States Census Bureau}},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2020-08-22 16:21:29 -0700},
	Note = {{Last checked on July 30, 2020}},
	Title = {American Community Survey},
	Url = {https://www.census.gov/acs/www/data/data-tables-and-tools/},
	Year = {2020},
	Bdsk-Url-1 = {http://api.census.gov/data/2015/acs5/profile/variables.html}}

@webpage{USDA,
	Author = {{USDA Food and Nutrition Service}},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Lastchecked = {{October 6th, 2017}},
	Month = {10},
	Title = {{Women, Infants, and Children (WIC)}},
	Url = {https://www.fns.usda.gov/wic/women-infants-and-children-wic},
	Year = {2017},
	Bdsk-Url-1 = {https://www.fns.usda.gov/wic/women-infants-and-children-wic}}

@article{vanderweele2008,
	Author = {VanderWeele, Tyler J},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistics in Medicine},
	Number = {11},
	Pages = {1934-1943},
	Publisher = {Wiley Online Library},
	Title = {Ignorability and stability assumptions in neighborhood effects research},
	Volume = {27},
	Year = {2008}}

@article{vaughan_hierarchy,
	Abstract = {Many researchers assessing the efficacy of educational programs face challenges due to issues with non-randomization and the likelihood of dependence between nested subjects. The purpose of the study was to demonstrate a rigorous research methodology using a hierarchical propensity score matching method that can be utilized in contexts where randomization is not feasible and dependence between subjects is a concern. Although propensity score matching is not new in helping to create quasi-experimental models, many studies limit propensity score matching to student-level variables. To address this limitation in educational research, this study extends propensity score matching to the next level so that hierarchical modeling techniques can be used to help minimize error due to the likelihood of dependence between nested students. A large-scale educational program that targets first-semester freshmen was used to illustrate the utility and value of the methodology. This type of program is typical in higher education where student self-selection creates difficulty in assessing its true effects on student achievement; however, by using a rigorous methodology, administrators can have higher confidence when making programmatic and budgetary decisions.},
	Author = {Vaughan, Angela L. and Lalonde, Trent L. and Jenkins-Guarnieri, Michael A.},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Research in Higher Education},
	Number = {6},
	Pages = {564-580},
	Title = {Assessing Student Achievement in Large-Scale Educational Programs Using Hierarchical Propensity Scores},
	Volume = {55},
	Year = {2014},
	Bdsk-Url-1 = {https://doi.org/10.1007/s11162-014-9329-8},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/s11162-014-9329-8}}

@article{verbitsky2012,
	Author = {Verbitsky-Savitz, Natalya and Raudenbush, Stephen W},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Epidemiologic Methods},
	Number = {1},
	Pages = {107-130},
	Publisher = {De Gruyter},
	Title = {Causal inference under interference in spatial settings: a case study evaluating community policing program in Chicago},
	Volume = {1},
	Year = {2012}}

@article{xiang2015,
	Author = {Xiang, Yun and Tarasawa, Beth},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of School Choice},
	Number = {2},
	Pages = {179-196},
	Publisher = {Taylor \& Francis},
	Title = {Propensity score stratification using multilevel models to examine charter school achievement effects},
	Volume = {9},
	Year = {2015}}

@article{yang_multilevel_gps,
	Author = {Yang, Shu and Imbens, Guido W and Cui, Zhanglin and Faries, Douglas E and Kadziola, Zbigniew},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Biometrics},
	Number = {4},
	Pages = {1055-1065},
	Publisher = {Wiley Online Library},
	Title = {Propensity score matching and subclassification in observational studies with multi-level treatments},
	Volume = {72},
	Year = {2016}}

@article{yang_cov_adjust,
	Author = {Yang, Wei and Joffe, Marshall M and Hennessy, Sean and Feldman, Harold I},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistics in Medicine},
	Number = {26},
	Pages = {4577-4589},
	Publisher = {Wiley Online Library},
	Title = {Covariance adjustment on propensity parameters for continuous treatment in linear models},
	Volume = {33},
	Year = {2014}}

@article{yen1993,
	Author = {Yen, Steven T},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {American Journal of Agricultural Economics},
	Number = {4},
	Pages = {884-895},
	Title = {Working wives and food away from home: the {Box-Cox} double hurdle model},
	Volume = {75},
	Year = {1993}}

@article{zhang2016,
	Abstract = { SummaryThe current statistical literature on causal inference is mostly concerned with binary or categorical exposures, even though exposures of a quantitative nature are frequently encountered in epidemiologic research. In this article, we review the available methods for estimating the dose--response curve for a quantitative exposure, which include ordinary regression based on an outcome regression model, inverse propensity weighting and stratification based on a propensity function model, and an augmented inverse propensity weighting method that is doubly robust with respect to the two models. We note that an outcome regression model often imposes an implicit constraint on the dose--response curve, and propose a flexible modeling strategy that avoids constraining the dose--response curve. We also propose two new methods: a weighted regression method that combines ordinary regression with inverse propensity weighting and a stratified regression method that combines ordinary regression with stratification. The proposed methods are similar to the augmented inverse propensity weighting method in the sense of double robustness, but easier to implement and more generally applicable. The methods are illustrated with an obstetric example and compared in simulation studies. },
	Author = {Zhiwei Zhang and Jie Zhou and Weihua Cao and Jun Zhang},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Statistical Methods in Medical Research},
	Number = {1},
	Pages = {315-335},
	Title = {Causal inference with a quantitative exposure},
	Volume = {25},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1177/0962280212452333}}

@article{zhu_boosting,
	Author = {Zhu, Yeying and Coffman, Donna L and Ghosh, Debashis},
	Date-Added = {2019-04-22 15:29:37 -0700},
	Date-Modified = {2019-04-22 15:29:37 -0700},
	Journal = {Journal of Causal Inference},
	Number = {1},
	Pages = {25-40},
	Title = {A boosting algorithm for estimating generalized propensity scores with continuous treatments},
	Volume = {3},
	Year = {2015}}

@comment{BibDesk Static Groups{
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<array>
	<dict>
		<key>group name</key>
		<string>Continuous Tx</string>
		<key>keys</key>
		<string>hirano_continuous,imai_causalGPS,schuler2016,vegetabile2020nonparametric,zhu_boosting,tbbicke2020entropy,kreif_gps_ml,kennedy2017,fong2018,CBGPS_time_varying</string>
	</dict>
	<dict>
		<key>group name</key>
		<string>Dataset</string>
		<key>keys</key>
		<string>crespi2012validity,phfewic,census,chaparro2014influences,wang2018developing,walls2013national,wic_data_mining,food_pacakage_change,hillier2012impact,nobari2013immigrant,rosenkranz2008</string>
	</dict>
	<dict>
		<key>group name</key>
		<string>Interference</string>
		<key>keys</key>
		<string>hong2006,kim2007,mccormick2013,vanderweele2008,kim_hierarchalpropensity,nickerson2008voting,halloran1995causal,verbitsky2012causal,forastiere2016identification,papadogeorgou2019causal,papadogeorgou2018adjusting</string>
	</dict>
	<dict>
		<key>group name</key>
		<string>Multinomial Treatment</string>
		<key>keys</key>
		<string>Brown2020,imbens_dose_resp,lechner2001</string>
	</dict>
	<dict>
		<key>group name</key>
		<string>Simultaneous Treatments</string>
		<key>keys</key>
		<string>crohns_combo_tx,cancer_combo_tx,hypertension_combo_tx,covid_combo_stebbing,covid_combo_gautret,covid_tx_review,covid_combo_gautret2</string>
	</dict>
	<dict>
		<key>group name</key>
		<string>Weight Trimming</string>
		<key>keys</key>
		<string>lee2011weight,huber2013performance,crump2009dealing,kang2007demystifying</string>
	</dict>
</array>
</plist>
}}

gha_license_id: MIT

gha_language: HTML


text:
@inproceedings{gcasr_2013_environmental_data,
 abbr = {Workshop},
 author = {Stasiuk, Paul and Läufer, Konstantin and Thiruvathukal, George K.},
 booktitle = {Proc. 2nd Greater Chicago Area System Research Workshop (GCASR), Chicago, Illinois},
 copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA)},
 html = {https://ecommons.luc.edu/cs_facpubs/65/},
 month = {May},
 pdf = {https://ecommons.luc.edu/cs_facpubs/65/},
 shorttitle = {GCASR 13},
 title = {Network Technologies Used to Aggregate Environmental Data (Poster)},
 url = {https://ecommons.luc.edu/cs_facpubs/65/},
 year = {2013}
}

license: MIT

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
/**
 * @fileoverview Makes a commit after the fix has been made.
 * @module tasks/make-post-fix-commit
 * @author Derek Lewis
 */

'use strict';

module.exports = (() => {
  const { sync } = require('cross-spawn');
  const repomap = require('../repomap.json');

  const opts = { stdio: 'inherit' };

  // Add the sample JavaScript sources.
  repomap.jsSampleSrcs.map((path) => {
    return sync('git', ['add', path], opts);
  });

  // Add the sample Markdown sources.
  repomap.mdSampleSrcs.map((path) => {
    return sync('git', ['add', path], opts);
  });

  sync('git', ['commit',
               '-m', 'chore: Post-fix commit'], opts);
})();

repository: DerekNonGeneric/node-samples

repo_name: DerekNonGeneric/node-samples

branch_name: refs/heads/master

gha_language: JavaScript

extension: js