ml-econometrics

What problem does it solve?

This Skill helps you estimate causal treatment effects when the set of controls is high-dimensional, where naive regression or plain LASSO can produce biased estimates and invalid standard errors.

Core Features & Use Cases

• Post-LASSO + valid inference: selects controls via Post-LASSO and then estimates treatment effects with OLS for more reliable inference in high-dimensional settings (e.g., Belloni-Chernozhukov-Hansen approach).
• Double/Debiased Machine Learning (DML) for ATE/ATT: uses cross-fitting with nuisance models (E[Y|X] and E[D|X]) to reduce regularization bias and recover valid, asymptotically normal inference.
• Heterogeneous effects via CausalForest (CATE): estimates conditional average treatment effects and surfaces feature importance for effect heterogeneity.
• Doubly robust AIPW estimator: provides a doubly-robust ATE estimator with cross-fitting and propensity clipping to mitigate extreme weights.
• Econometric use cases: program evaluation, policy impact analysis, and difference-in-differences–style workflows where rich covariates are required and causal identification depends on correct nuisance estimation.

Quick Start

Use the ml-econometrics skill to estimate the treatment effect (ATE) and uncertainty from your dataset by running Post-LASSO and then applying DML PLR with cross-fitting on the same outcome, treatment, and control variables.