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SKILL.md
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)
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
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.
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.
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, residual balance
DML-IRM
ATE (binary D)
doubleml, econml
DoubleML
~500
Propensity AUC, trim threshold
DML-PLIV
LATE
doubleml, econml
DoubleML
~1,000
Effective F-stat
Causal Forest
CATE(x)
econml
grf
~2,000
Calibration test, ATE match
DR-Learner
CATE(x)
econml.dr
manual/grf
~1,000
Propensity calibration
PDS-LASSO
ATE (high-dim X)
sklearn + manual
hdm
~200
Union size, penalty sensitivity
X-Learner
CATE (imbalanced D)
econml
manual
~1,000
Compare to DR-Learner
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), post-LASSO + many instruments = sparse instrument selection then 2SLS. Details in references/connections-traditional.md.
Integration with Plugin
Agents:econometric-reviewer (post-estimation review, table/code consistency), identification-critic (IV/PLIV assumptions), numerical-auditor (convergence, seeding, Monte Carlo validation).
Cross-references:empirical-playbook skill → sensitivity-analysis.md (specification curve over ML choices), empirical-playbook skill → diagnostic-battery.md (nuisance R², overlap, calibration), numerical-auditor agent (synthetic data with known CATE).
Relationship to causal-inference skill: Use causal-inference to establish identification; use causal-ml for implementation with high-dimensional controls or when heterogeneity is primary. Complements, not substitutes.
Reference Files
references/dml.md — Full DML implementation: PLR, IRM, PLIV with econml/DoubleML, cross-fitting, diagnostics
