Econml 模型配置

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作者仓库星标 307
许可证 MIT
作者仓库 zorai

解读按原文结构重写，命令、链接、术语均保留；右侧可核对作者原始 SKILL.md

原文结构

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…
Installation：原文以此为独立章节。
Double ML (Linear)：原文以此为独立章节。

适用与边界

当前原文没有单列适用场景。
当前原文没有单列不适用场景或限制。

原文中的明确线索

要点：「EconML is a Microsoft library for causal inference and heterogeneous treatment effect estimation using machine learning.」
文件与命令：github.com/py-why/EconML

流狐整理：以上内容来自当前 SKILL.md 的章节与原词；未补写作者没有声明的工具、兼容性或能力。

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

流狐档案作者与许可取自来源；运行、权限和网络为流狐检测或估算

流狐分类: 通用 · econml · causal-inference · heterogeneous-treatment-effects
作者声明 Agent: 未找到明确声明；不据此推断已兼容或已测试
静态检查: 100 / 100 · 启发式扫描，不代表运行安全
作者 / 版本 / 许可: @mkurman · MIT
流狐 Token 估算: 低消耗
流狐接入估算: 即装即用
是否需要外部 API Key: 未发现要求
检测到的系统要求: 未声明
底层运行要求: Python
检测到的文件与系统行为: 只读
检测到的网络行为: 仅限本地
安装命令数: 无（仅作为资料）

档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。

输出预览 econml.preview

作者没有在当前 SKILL.md 中定义固定输出样例。

SKILL.md SKILL.md

## 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

```bash
uv pip install econml
```

## Double ML (Linear)

```python
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

```python
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](https://econml.azurewebsites.net/)
- [EconML GitHub](https://github.com/py-why/EconML)

证据边界与执行链路

作者原文负责流程事实；流狐只索引当前章节、要点、文件与命令。

章节    -> Overview → Installation → Double ML (Linear) → Causal Forest → References
要点    -> EconML is a Microsoft library for causal inference and heterogeneous treatment effect estimation using machine learning.
文件/命令 -> github.com/py-why/EconML
内容 SHA-256 -> ffc705ea4493