README.md

metadata

title: Causal Forge
emoji: 🧪
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false

Causal Forge

Causal Forge is a ground-truth causal inference laboratory. Its structural causal model creates confounding, heterogeneous treatment effects, nonlinear outcomes, and known counterfactuals. The benchmark measures whether estimators recover the population average treatment effect rather than merely predicting observed outcomes.

The evaluation compares:

• the unadjusted difference in observed group means;
• inverse-propensity weighting (IPW);
• outcome regression with separate treatment/control response surfaces;
• the augmented IPW (AIPW) doubly robust estimator;
• four nuisance-model regimes where the propensity model, outcome model, both, or neither receive the correct nonlinear feature basis.

The exact potential outcomes and treatment probabilities are retained only because this is a synthetic benchmark. They make estimator bias directly measurable.

Verified results

The benchmark ran 100 independent replications with 3,000 observations and five-fold cross-fitting in each replication.

Nuisance-model regime	Naive MAE	IPW MAE	Outcome MAE	AIPW MAE	AIPW bias
Both correct	1.1109	0.0644	0.0388	0.0426	0.0004
Propensity misspecified	1.1109	0.1342	0.0388	0.0414	0.0008
Outcome misspecified	1.1109	0.0644	0.2688	0.0481	0.0078
Both misspecified	1.1109	0.1342	0.2688	0.3953	0.3953

This demonstrates the intended double-robustness boundary: AIPW remains accurate when either the treatment or outcome nuisance model is correct, but not when both are wrong. Results are Monte Carlo measurements on this synthetic SCM, not claims about arbitrary real-world observational data.

Reproduce

uv run python projects/causal-forge/train.py

Hosted showcase

This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.