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

Critical point: Causal ML does not relax identification assumptions. If you need a valid instrument, parallel trends, or no unmeasured confounding, those must still hold.

Double Machine Learning (DML)

DML (Chernozhukov et al. 2018) fixes regularization bias in naive ML-in-regression. Partial out controls X from both Y and D using separate ML nuisance models, then regress residuals. Two properties: Neyman orthogonality (moment condition locally insensitive to nuisance error) and cross-fitting (prevents overfitting bias).

PLR (Partially Linear Regression): $Y = \theta D + g(X) + \varepsilon$. Workhorse for continuous or binary D with ATE under selection on observables. IRM (Interactive Regression Model): relaxes additive separability for binary D with heterogeneous effects.

Full implementation (Python/R code, cross-fitting from scratch, diagnostics) in

references/dml.md

Causal Forests

Causal forests (Wager-Athey 2018; Athey-Tibshirani-Wager 2019) estimate CATE $\tau(x) = E[Y(1)-Y(0)|X=x]$ using honest forests (structure learned on one subsample, effects estimated on another). Use when CATE is the primary estimand and n $\geq$ 2,000. Always run the calibration test before reporting heterogeneity.

R (

grf

econml

references/grf-meta-learners.md

Meta-Learners

Decompose CATE estimation into supervised learning sub-problems. DR-Learner (Kennedy 2023): best properties when both nuisance models are well-specified. T-Learner: simplest baseline. X-Learner: designed for imbalanced treatment. For applied work: DR-Learner primary, T-Learner benchmark. Large disagreement signals nuisance model problems.

All implementations in

references/grf-meta-learners.md

High-Dimensional Controls

PDS-LASSO (Belloni-Chernozhukov-Hansen 2014): separate LASSOes of Y on X and D on X, union of selected variables, then OLS. Works at moderate n (~200 with sparse confounders). See

references/high-dim-cross-fitting.md

HTE Inference

Before reporting CATE, test for genuine heterogeneity using BLP calibration test. Do not report heterogeneous effects if calibration test fails (p > 0.10). See

references/hte-inference.md

Method Selection Guide

Decision Heuristic

1. n < 500? → Use standard methods (causal-inference skill)
2. High-dim controls (p > 20), want ATE? → PDS-LASSO or DML-PLR; binary D → DML-IRM
3. CATE is primary estimand? → Causal Forest (large n) or DR-Learner (doubly robust)
4. Endogenous treatment with instrument? → DML-PLIV
5. Treatment is rare/imbalanced? → X-Learner
6. Quick benchmark? → Always compute T-Learner as baseline

Full Method Comparison

doubleml

econml

DoubleML

doubleml

econml

DoubleML

doubleml

econml

DoubleML

econml

grf

econml.dr

grf

sklearn

hdm

econml

Limitations to State Explicitly

• ML needs data: Causal forests need n $\geq$ 2,000; DML needs n $\geq$ 500. Below these, use parametric methods.
• Identification is not relaxed: ML is better nuisance estimation, not weaker assumptions.
• CATE inference is hard: Individual-level CIs are conservative; policy targeting requires care.
• Publication: DML and causal forests are mainstream in top applied micro journals. Compare to traditional estimators.

Connections to Traditional Methods

Causal ML nests traditional estimators: DML with linear nuisance = OLS (Frisch-Waugh), DML + IV = PLIV, causal forests + instrument = heterogeneous LATE (

grf::instrumental_forest

references/connections-traditional.md

Integration with Plugin

Agents:

econometric-reviewer

identification-critic

numerical-auditor

Cross-references:

empirical-playbook

sensitivity-analysis.md

empirical-playbook

diagnostic-battery.md

numerical-auditor

Relationship to

causal-inference

causal-inference

causal-ml

Reference Files

• references/dml.md

  — Full DML implementation: PLR, IRM, PLIV with econml/DoubleML, cross-fitting, diagnostics

• references/grf-meta-learners.md

  — Causal forests (grf/econml), DR/T/S/X-Learner, calibration tests

• references/high-dim-cross-fitting.md

  — PDS-LASSO, Belloni-Chernozhukov-Hansen, cross-fitting protocols

• references/hte-inference.md

  — Calibration tests, individual CATE CIs, BLP projections, subgroup analysis

• references/connections-traditional.md

  — DML-OLS equivalence, PLIV, instrumental forests, post-LASSO