domains / families / research / causal-ml

causal-ml

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This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.

When to use

• Estimating treatment effects with high-dimensional controls (p large relative to n)
• Interested in heterogeneous treatment effects (CATE) as a primary estimand
• Applying ML for flexible nuisance function estimation within a causal framework
• Implementing cross-fitting, sample splitting, or Neyman-orthogonal estimators
• Using `econml`, `DoubleML`, or `grf` packages
• Sample is small (n < 500 — ML nuisance models need data)
• A well-specified parametric model is available and defensible
• The task is standard IV/DiD/RDD without high-dimensional controls (use `causal-inference` skill)

When not to use

• Small samples (n < 500)
• Well-specified parametric models
• Standard IV/DiD/RDD without high-dimensional controls
• Structural modeling
• Formal identification proofs

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