ml-econometrics

name	ml-econometrics
description	Use this Skill for machine learning in econometrics: Post-LASSO for high-dimensional controls, Double/Debiased ML (DML) for ATE/ATT, cross-fitting, and valid post-selection inference.
tags	["economics","machine-learning","LASSO","double-debiased-ML","high-dimensional","causal-inference"]
version	1.0.0
authors	[{"name":"awesome-rosetta-skills contributors","github":"@xjtulyc"}]
license	MIT
platforms	["claude-code","codex","gemini-cli","cursor"]
dependencies	{"python":["doubleml>=0.6","econml>=0.15","scikit-learn>=1.2","numpy>=1.23","pandas>=1.5"]}
last_updated	2026-03-18
status	stable

Machine Learning Econometrics

TL;DR — When controls are high-dimensional (many covariates relative to sample size), use Post-LASSO for variable selection and Double/Debiased ML (DML) for valid causal inference on treatment effects. Use EconML CausalForest for heterogeneous treatment effects (CATE).

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When to Use

Situation	Recommended Method
Many controls, single treatment, want ATE	Post-LASSO + OLS or DML-PLR
Need to partial out high-dimensional X from D and Y	Robinson (1988) / DML
Heterogeneous treatment effects across subgroups	CausalForest (EconML)
Program evaluation with rich covariate set	AIPW estimator
Valid inference after LASSO selection	Post-LASSO (Belloni et al. 2012)

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Background

Problem: Regularization Bias in LASSO

When estimating β_D in Y = β_D D + X β_X + ε with high-dimensional X, directly using LASSO on the full model introduces regularization bias into β̂_D. This happens because LASSO shrinks all coefficients, including the one on the treatment D.

Naive LASSO estimate: Biased, no valid standard errors.

Post-LASSO (Belloni-Chernozhukov-Hansen 2014)

1. Run LASSO of Y on (D, X) to select a set S of controls.
2. Run OLS of Y on D and the selected subset X_S only.
3. Standard OLS inference is valid on the Post-LASSO estimator.

This two-step procedure recovers valid inference when the true model is approximately sparse (few truly important controls).

Frisch-Waugh-Lovell and Robinson's Estimator

By the FWL theorem, β_D in Y = β_D D + X β_X + ε equals the coefficient from regressing M_X Y on M_X D, where M_X is the residual maker.

Robinson (1988) extended this to semiparametric partially linear models: Y = θ D + g(X) + ε D = m(X) + v

The estimator: θ̂ = [Σ (D_i - m̂(X_i)) (Y_i - ĝ(X_i))] / [Σ (D_i - m̂(X_i))²]

where ĝ and m̂ can be any ML estimators.

Double/Debiased ML (Chernozhukov et al. 2018)

DML corrects for regularization bias via cross-fitting:

1. Split data into K folds.
2. For each fold k:
   • Fit ĝ_{-k}(X) = E[Y|X] and m̂_{-k}(X) = E[D|X] on all folds except k.
   • Compute residuals for fold k: ỹ_i = Y_i - ĝ_{-k}(X_i), d̃_i = D_i - m̂_{-k}(X_i).
3. Regress ỹ on d̃ using all pooled residuals.