ml-causal

name

ml-causal

description

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

Machine Learning for Causal Inference Skill

This skill covers modern ML-based causal inference methods: Causal Forests (GRF) for heterogeneous treatment effects, Double/Debiased Machine Learning (DML) for partially linear models, and LASSO-based variable selection. These methods combine the flexibility of ML with the rigor of econometric identification.

When to Use ML Causal Methods

Goal	Method
Estimate average treatment effect with many controls	Double ML (DML)
Discover treatment effect heterogeneity	Causal Forest (GRF)
Variable selection for high-dimensional controls	Post-LASSO
Best linear predictor of CATE	BLP analysis
Subgroup with largest/smallest effects	CLAN analysis

Key principle: ML is used for nuisance parameter estimation (predicting Y and D), not for identifying causal effects directly. Identification still requires valid research design (RCT, IV, DID, etc.).

Double/Debiased Machine Learning (DML)

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins (2018)

Partially Linear Model

Y = θ·D + g(X) + ε     (structural equation)
D = m(X) + v           (treatment equation)

θ = causal parameter of interest
g(X), m(X) = unknown nuisance functions estimated by ML

DML Procedure

1. Cross-fitting: Split sample into K folds (typically K=5)
2. Nuisance estimation: On each fold k, use remaining folds to estimate ĝ(X) and m̂(X) using ML
3. Residualize: Compute Ỹ = Y − ĝ(X) and D̃ = D − m̂(X)
4. Final estimation: Regress Ỹ on D̃ to obtain θ̂

R — DoubleML

# R — DoubleML package
library(DoubleML)
library(mlr3)
library(mlr3learners)

# Define data
dml_data <- DoubleMLData$new(
  data = df,
  y_col = "outcome",
  d_cols = "treatment",
  x_cols = c("x1", "x2", "x3", "x4", "x5")
)

# Choose ML methods for nuisance estimation
ml_g <- lrn("regr.ranger", num.trees = 500)   # for E[Y|X]
ml_m <- lrn("classif.ranger", num.trees = 500) # for E[D|X]

# Fit DML (partially linear model)
dml_plr <- DoubleMLPLR$new(dml_data, ml_g ml_m
                            n_folds   n_rep  
dml_plrfit
printdml_plr



dml_irm  DoubleMLIRMnewdml_data ml_g ml_m
                            n_folds   score  
dml_irmfit
printdml_irm

Python — DoubleML

# Python — DoubleML
import doubleml as dml
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

# Define data
dml_data = dml.DoubleMLData(
    df, y_col='outcome', d_cols='treatment',
    x_cols=['x1', 'x2', 'x3', 'x4', 'x5']
)

# Nuisance learners
ml_g = RandomForestRegressor(n_estimators=500, max_depth=5)
ml_m = RandomForestClassifier(n_estimators=500, max_depth=5)

# Partially Linear Regression
dml_plr = dml.DoubleMLPLR(dml_data, ml_g, ml_m,
                            n_folds=5, n_rep=10)
dml_plr.fit()
print(dml_plr.summary)

# Confidence interval
print(dml_plr.confint())

Stata — ddml

* Stata — ddml (Ahrens et al. 2024)
ssc install ddml
ssc install pystacked

* Partially linear model with cross-fitting
ddml init partial, kfolds(5) reps(10)
ddml E[outcome]: pystacked outcome x1 x2 x3 x4 x5, ///
    type(reg) methods(rf gradboost lassocv)
ddml E[treatment]: pystacked treatment x1 x2 x3 x4 x5, ///
    type(class) methods(rf gradboost lassocv)
ddml crossfit
ddml estimate, robust

Causal Forest (Generalized Random Forest)

Athey, Tibshirani & Wager (2019)

Estimates Conditional Average Treatment Effects (CATE): τ(x) = E[Y(1) − Y(0) | X = x]

R — grf

# R — Generalized Random Forest
library(grf)

# Prepare data
X <- as.matrix(df[, c("x1", "x2", "x3", "x4", "x5")])
Y <- df$outcome
W <- df$treatment

# Fit causal forest
cf <- causal_forest(X, Y, W,
                    num.trees = 4000,
                    honesty = TRUE,          # honest estimation
                    tune.parameters = "all") # auto-tune

# Average treatment effect (ATE)
ate <- average_treatment_effect(cf, target.sample = "all")
cat("ATE:", ate["estimate"  ate 


att  average_treatment_effectcf target.sample  
cat att  att 


cate  predictcf estimate.variance  
dfcate_hat  catepredictions
dfcate_se   catevariance.estimates


varimp  variable_importancecf
varimp  colnamesX
sortvarimp decreasing  

Python — econml / grf

# Python — EconML (Microsoft)
from econml.dml import CausalForestDML

# Fit causal forest via DML
cf = CausalForestDML(
    model_y=RandomForestRegressor(n_estimators=500),
    model_t=RandomForestClassifier(n_estimators=500),
    n_estimators=4000,
    cv=5,
    random_state=42
)
cf.fit(Y=df['outcome'].values,
       T=df['treatment'].values,
       X=df[['x1', 'x2', 'x3', 'x4', 'x5']].values)

# ATE
ate = cf.ate_inference()
print(f"ATE: {ate.mean_point:.4f} (SE: {ate.stderr_mean:.4f})")

# CATE predictions
cate = cf.effect(df[['x1', 'x2', 'x3', 'x4', 'x5']].values)
df['cate_hat'] = cate

BLP Analysis (Best Linear Predictor)

Tests whether CATE varies with observables. From Chernozhukov, Demirer, Duflo & Fernandez-Val (2020).

# R — BLP of CATE
library(grf)

# After fitting causal_forest cf:
blp <- best_linear_projection(cf, A = X)
print(blp)

# Interpretation:
# - Intercept: average effect
# - Coefficients: how CATE varies with each covariate
# - If all coefficients ≈ 0 → homogeneous treatment effect

CLAN Analysis (Classification Analysis)

Identifies subgroups with highest/lowest treatment effects.

# Sorted Group Average Treatment Effects (GATES)
# Split sample by predicted CATE quartiles
df$cate_quartile <- cut(df$cate_hat,
                         breaks = quantile(df$cate_hat, c(0, 0.25, 0.5, 0.75, 1)),
                         labels = c("Q1 (lowest)", "Q2", "Q3", "Q4 (highest)"),
                         include.lowest = TRUE)

# GATES regression
library(fixest)
gates <- feols(outcome ~ i(cate_quartile, treatment),
               data = df, vcov = "HC1")
summarygates



clan_table  df 
  group_bycate_quartile 
  summariseacrossx1 x2 x3 age income mean
printclan_table

AIPW / Augmented IPW (Doubly Robust Estimator)

Combines outcome regression and propensity score weighting. Doubly robust: consistent if either the outcome model or the propensity score model is correctly specified (but not necessarily both). Particularly natural for binary treatment and binary/continuous outcomes.

Estimator:

τ_AIPW = E[ μ₁(X) − μ₀(X) + D(Y − μ₁(X))/e(X) − (1−D)(Y − μ₀(X))/(1−e(X)) ]

where μ_d(X) = E[Y|D=d, X] and e(X) = P(D=1|X) are estimated by ML.

Difference from DML: AIPW is more natural for binary treatment/outcome; DML is better suited for continuous treatment or partially linear structural models. Both use cross-fitting.

# Python — AIPW / DR Learner (EconML)
from econml.dr import DRLearner
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.linear_model import LogisticRegressionCV
import numpy as np

# Data preparation
Y = df['outcome'].values
T = df['treatment'].values          # binary: 0/1
X = df[['x1', 'x2', 'x3']].values

# DRLearner implements AIPW with cross-fitting for CATE estimation
dr_learner = DRLearner(
    model_propensity=LogisticRegressionCV(cv=5),   # propensity score e(X)
    model_regression=RandomForestRegressor(n_estimators=200),  # outcome μ_d(X)
    model_final=RandomForestRegressor(n_estimators=200),       # CATE model
    cv=5,
    random_state=42
)
dr_learner.fit(Y, T, X=X)

# ATE via AIPW
ate = dr_learner.ate_inference(X=X)
print(f"AIPW ATE: {ate.mean_point:.4f} (SE: {ate.stderr_mean:.4f})")
print(f"95% CI: {ate.conf_int_mean()}")

# CATE predictions
cate = dr_learner.effect(X)

# R — AIPW using AIPW package
# install.packages("AIPW")
library(AIPW)
library(SuperLearner)

aipw_obj <- AIPW$new(
  Y = df$outcome,
  A = df$treatment,
  W = df[, c("x1", "x2", "x3")],
  Q.SL.library = c("SL.ranger", "SL.glm"),   # outcome model
  g.SL.library = c("SL.ranger", "SL.glm"),   # propensity model
  k_split = 5,       # cross-fitting folds
  verbose = FALSE
)
aipw_obj$fit()
aipw_objsummary

Meta-Learners for CATE Estimation

Meta-learners are general frameworks for estimating CATE that wrap any base ML model. They differ in how they use the treatment variable.

Learner	Approach	Best When
S-Learner	Single model: fit μ(X, D), then CATE = μ(X,1) − μ(X,0)	Simple baseline; may shrink CATE to zero if D is weak signal
T-Learner	Two separate models: μ₁(X) and μ₀(X)	Unequal sample sizes; less regularization shrinkage on treatment
X-Learner	Imputes counterfactuals, then fits CATE on imputed residuals	Unbalanced treatment (very few treated or controls)
R-Learner	Residualizes Y and D, then fits CATE on residuals	High confounding; closely related to DML

# Python — Meta-Learners via EconML
from econml.metalearners import TLearner, SLearner, XLearner
from econml.dml import LinearDML
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
import numpy as np

Y = df['outcome'].values
T = df['treatment'].values
X = df[['x1', 'x2', 'x3']].values

# --- T-Learner ---
t_learner = TLearner(models=RandomForestRegressor(n_estimators=500))
t_learner.fit(Y, T, X=X)
cate_t = t_learner.effect(X)
print(f"T-Learner ATE: {np.mean(cate_t):.4f}")

# --- S-Learner ---
s_learner = SLearner(overall_model=RandomForestRegressor(n_estimators=500))
s_learner.fit(Y, T, X=X)
cate_s = s_learner.effect(X)

# --- X-Learner ---
x_learner = XLearner(
    models=RandomForestRegressor(n_estimators=500),
    propensity_model=RandomForestClassifier(n_estimators=500)
)
x_learner.fit(Y, T, X=X)
cate_x = x_learner.effect(X)

# --- R-Learner (via LinearDML with non-parametric final stage) ---
r_learner = LinearDML(
    model_y=RandomForestRegressor(n_estimators=500),
    model_t=RandomForestClassifier(n_estimators=500),
    cv=5, random_state=42
)
r_learner.fit(Y, T, X=X, W=None)
cate_r = r_learner.effect(X)

# Compare ATE across meta-learners
print(f"S: {np.mean(cate_s):.4f} | T: {np.mean(cate_t):.4f} | "
      f"X: {np.mean(cate_x):f} | R: ")

# R — X-Learner using grf building blocks
library(grf)

# Step 1: T-Learner stage
X_mat <- as.matrix(df[, c("x1", "x2", "x3")])
Y <- df$outcome; W <- df$treatment

rf1 <- regression_forest(X_mat[W==1, ], Y[W==1])  # treated
rf0 <- regression_forest(X_mat[W==0, ], Y[W==0])  # control

# Step 2: Impute counterfactuals
mu1 <- predict(rf1, X_mat)$predictions
mu0 <- predictrf0 X_matpredictions


D1  YW  predictrf0 X_matWpredictions  
D0  predictrf1 X_matWpredictions  YW  


tau1  regression_forestX_matW D1
tau0  regression_forestX_matW D0


e_hat  regression_forestX_mat Wpredictions  
cate_x  e_hat  predicttau0 X_matpredictions 
            e_hat  predicttau1 X_matpredictions
cat meancate_x 

LASSO for Variable Selection

Post-LASSO (Belloni, Chernozhukov & Hansen 2014)

Use LASSO to select controls, then run OLS with selected variables.

# R — Post-LASSO
library(hdm)

# Post-double-selection LASSO for ATE
pds <- rlassoEffect(x = X, y = Y, d = W, method = "double selection")
summary(pds)
# Reports: coefficient, SE, t-stat, CI

# Python — Post-LASSO
from sklearn.linear_model import LassoCV
import statsmodels.api as sm

# Step 1: LASSO on Y ~ X to select controls
lasso_y = LassoCV(cv=5).fit(X, Y)
selected_y = np.where(lasso_y.coef_ != 0)[0]

# Step 2: LASSO on D ~ X to select controls
lasso_d = LassoCV(cv=5).fit(X, W)
selected_d = np.where(lasso_d.coef_ != 0)[0]

# Step 3: Union of selected variables
selected = np.union1d(selected_y, selected_d)

# Step 4: OLS with selected controls
X_selected = sm.add_constant(np.column_stack([W, X[:, selected]]))
ols_result = sm.OLS(Y, X_selected).fit(cov_type='HC1')
print(f"Post-LASSO ATE: {ols_result.params[1]:.4f} (SE: {ols_result.bse[1]:.4f})")

* Stata — Post-double-selection LASSO
ssc install lassopack

* pdslasso: post-double-selection
pdslasso outcome treatment (x1-x50), robust

Diagnostics and Validation

Calibration Test for Causal Forest

# Test forest calibration: does the forest detect heterogeneity?
calibration <- test_calibration(cf)
print(calibration)
# Row 1 (mean forest prediction): should be significant → forest detects an effect
# Row 2 (differential forest prediction): significant → heterogeneity exists

Cross-Validated Performance

# Out-of-bag predictions (built into grf)
oob_predictions <- predict(cf)$predictions  # uses OOB by default
cor(oob_predictions, df$true_cate)  # if true CATE known (simulation)

Reporting Standards

1. Method description: State the ML method used for nuisance estimation (RF, LASSO, boosting)
2. Cross-fitting: Report number of folds (K) and repetitions
3. ATE with CI: Report point estimate, SE, 95% CI
4. Heterogeneity evidence: BLP table, GATES plot, variable importance
5. Robustness: Compare DML with different ML methods; compare GRF with traditional subgroup analysis

Key sentence template (DML):

"We estimate the treatment effect using Double Machine Learning (Chernozhukov et al. 2018) with random forests for nuisance estimation, 5-fold cross-fitting, and 10 repetitions. The estimated ATE is [β] (SE = [se], 95% CI: [lb, ub])."

Key sentence template (Causal Forest):

"We estimate heterogeneous treatment effects using a causal forest (Athey et al. 2019) with [N] trees and honest splitting. The calibration test confirms significant heterogeneity (p = [p]). Units in the top quartile of predicted CATE have an estimated effect of [β_Q4] compared to [β_Q1] in the bottom quartile."

Common Pitfalls

• Using ML for identification: ML estimates nuisance parameters, not causal effects. You still need exogenous variation (RCT, IV, etc.)
• Overfitting CATE: Always use honest estimation (separate splitting and estimation samples)
• Interpreting variable importance causally: Variable importance in GRF shows predictive power for heterogeneity, not causal mediation
• Ignoring cross-fitting: Without cross-fitting, DML estimates are biased

See references/ml-causal-reference.md for IV-based causal forests, DML with IV, and simulation studies.

Related Skills & Commands

• iv-estimation: DML can be combined with IV for endogenous treatment
• did-analysis: ML methods can complement DID for heterogeneity analysis
• matching: AIPW is closely related to DML
• /robustness: Compare ML-based and traditional estimates
• /diagnose: Check model diagnostics after estimation