Causal Machine Learning

Causal Machine Learning

Reference for semiparametric ML estimators: DML with cross-fitting, generalized random forests, debiased regularization, and nuisance function approximation. Covers Neyman-orthogonal moment conditions, sample splitting, plug-in bias correction, and heterogeneous treatment effects.

When to Use This Skill

Use when the user is:

• 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

Skip when:

• 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)
• Structural modeling is needed (use structural-modeling skill)
• The task needs formal identification proof (use identification-proofs skill)

Where to Start

• Choosing a method? Jump to Method Selection Guide
• ATE with many controls? See references/dml.md
• Heterogeneous treatment effects? See references/grf-meta-learners.md
• Variable selection for controls? See references/high-dim-cross-fitting.md
• Reporting HTE results? See references/hte-inference.md
• Connecting to traditional methods? See references/connections-traditional.md

Causal ML vs Traditional Methods