/causal-ml

name: causal-ml
argument-hint: "<estimator or method choice>"
description: >-
  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.

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](#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`

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Causal ML vs Traditional Methods

| Dimension | Traditional (IV, DiD, RDD) | Causal ML | |-----------|--------------------------|-----------| | Functional form | Parametric | Nonparametric / semi-parametric | | High-dimensional controls | Problematic | Native support | | Heterogeneous effects | Secondary (subgroup analysis) | Primary estimand (CATE) | | Sample requirements | Moderate N | ML nuisance needs large N | | Identification | Explicit (IV, DiD, RCT) | Same assumptions — ML is estimation, not identification |

**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.

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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`) and Python (`econml`) implementations, ATE/ATT extraction, BLP projections in `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`.

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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

| Method | Estimand | Python | R | Min n | Key diagnostic | |--------|----------|--------|---|-------|----------------| | DML-PLR | ATE | `doubleml`, `econml` | `DoubleML` | ~500 | Nuisance R², res