econml

name	econml
description	EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep IV, and metalearners (S-Learner, T-Learner, X-Learner). Orthogonal learning for causal effects from observational data.
tags	["econml","causal-inference","heterogeneous-treatment-effects","causal-forest","microsoft","econometrics","zorai"]

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}")

References

• EconML docs
• EconML GitHub