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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/mkurman/zorai --skill econml命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
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正在显示 SKILL.md
| 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"] |
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}")