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voidly-causal-forest-hte-v1
emperor-mew/voidly-causal-forest-hte-v1
voidly-causal-forest-hte-v1 is a machine learning model from emperor-mew. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for econml. The card lists the license as cc-by-4.0.
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Updated May 22, 2026
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At a glance
- Library
- econml
- License
- cc-by-4.0
- Access
- Public
- Created
- May 22, 2026
- Updated
- May 22, 2026
- SHA
- 7d271719
Model card
From the Hugging Face model README
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
- Library
- econml
- License
- cc-by-4.0
- Created
- May 22, 2026
- Updated
- May 22, 2026