econml
SkillDev toolsEconML (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.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the econml skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/econml/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
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econml- Source
- github.com/mkurman/zorai