---
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

```bibtex
@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