Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →mkurman/econml
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.
Passed 19 of 23 test cases with the skill, and 14 of 23 without it.
and 3 more in the full report
6 fixed13 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When fitting an EconML CausalForest model on covariates matrix X, treatment array T, and response vector Y, what positional argument order does the fit method take?
| Checked | Without skill | With skill |
|---|---|---|
| Passes positional arguments to fit in the exact order X, T, Y. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
uv pip install econml
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}")
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}")
test cases passed, vs 14 / 23 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.