metadata
license: cc-by-4.0
library_name: econml
tags:
- causal-inference
- hte
- econml
- voidly
- atlas
Voidly Atlas Causal Forest HTE v1
Version: v1 | License: CC BY 4.0
Heterogeneous treatment effects of upcoming elections on censorship risk.
Headline finding
ATE: +9.6 percentage points lift in 7-day risk in the 30-day window before an election.
Honest caveats
- Heterogeneity is wide — some regimes show +30pp (Venezuela, Belarus) while stable democracies show ~0pp.
- Election event metadata is hand-curated from Wikipedia + GDELT — coverage gaps in 2026 Q1 will shift the ATE.
- Confounders not exhaustively addressed (no instrument); use as descriptive HTE, not causal effect estimate for production decisions.
Citation
@misc{voidly_voidly_causal_forest_hte_v1,
title = {Voidly Atlas: voidly-causal-forest-hte-v1 (v1)},
author = {Voidly},
year = {2026},
url = {https://huggingface.co/emperor-mew/voidly-causal-forest-hte-v1},
note = {Open censorship-research ML stack. CC BY 4.0.}
}
Method foundation: Athey & Wager 2019 — Generalized Random Forests