ml-causal
Installation
SKILL.md
Machine Learning for Causal Inference Skill
This skill covers modern ML-based causal inference methods: Causal Forests (GRF) for heterogeneous treatment effects, Double/Debiased Machine Learning (DML) for partially linear models, and LASSO-based variable selection. These methods combine the flexibility of ML with the rigor of econometric identification.
When to Use ML Causal Methods
| Goal | Method |
|---|---|
| Estimate average treatment effect with many controls | Double ML (DML) |
| Discover treatment effect heterogeneity | Causal Forest (GRF) |
| Variable selection for high-dimensional controls | Post-LASSO |
| Best linear predictor of CATE | BLP analysis |
| Subgroup with largest/smallest effects | CLAN analysis |
Key principle: ML is used for nuisance parameter estimation (predicting Y and D), not for identifying causal effects directly. Identification still requires valid research design (RCT, IV, DID, etc.).
Double/Debiased Machine Learning (DML)
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins (2018)