Overview
EconML is a Microsoft library for causal inference and heterogeneous treatment effect estimation using machine learning. Implements Double ML, Causal Forest, DML, IV methods, and orthogonal statistical learning. Designed for observational data where treatment effects vary across individuals.
Installation
uv pip install econml
Double ML (Linear)
from econml.dml import LinearDML
import numpy as np
X = np.random.randn(500, 5) # features
T = np.random.randn(500) # treatment
Y = T * (0.5 + X[:, 0]) + np.random.randn(500) # outcome
est = LinearDML(model_y="auto", model_t="auto", discrete_treatment=False)
est.fit(Y, T, X=X)
print(f"ATE: {est.ate():.3f} ± {est.ate_inference().stderr:.3f}")
Causal Forest
from econml.grf import CausalForest
cf = CausalForest(n_estimators=100, min_samples_leaf=10)
cf.fit(X, T, Y)
treatment_effects = cf.effect(X)
print(f"Heterogeneous effects range: {treatment_effects.min():.3f} to {treatment_effects.max():.3f}")