“因果森林 Causal Forest”的版本间的差异
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1587causalai（讨论 | 贡献） （快速撰写） |
1587causalai（讨论 | 贡献） （quick start） |
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| + | 待补充 | ||
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| + | $$\hat{\theta}(x) = \argmin_{\theta} \sum_{i=1}^n K_x(X_i)\cdot \left( Y_i - \hat{q}(X_i, W_i) - \theta \cdot (T_i - \hat{f}(X_i, W_i)) \right)^2$$ | ||
| + | ==算法== | ||
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| + | [[文件:广义随机森林.png|缩略图]] | ||
| + | 待补充 | ||
.... | .... | ||
| − | == 算法实现 == | + | ==算法实现== |
Python 包 econml 和 R 包 grf 都有实现该算法。 | Python 包 econml 和 R 包 grf 都有实现该算法。 | ||
| − | == 参考文献 == | + | ==参考文献== |
| − | === Recursive Partitioning for Heterogeneous Causal Effects - arXiv === | + | ===Recursive Partitioning for Heterogeneous Causal Effects - arXiv=== |
''[Submitted on 5 Apr 2015 (v1), last revised 30 Dec 2015 (this version, v3)]'' | ''[Submitted on 5 Apr 2015 (v1), last revised 30 Dec 2015 (this version, v3)]'' | ||
| − | === Estimation and Inference of Heterogeneous Treatment Effects ... === | + | ===Estimation and Inference of Heterogeneous Treatment Effects ... === |
''[Submitted on 14 Oct 2015 (v1), last revised 10 Jul 2017 (this version, v4)]'' | ''[Submitted on 14 Oct 2015 (v1), last revised 10 Jul 2017 (this version, v4)]'' | ||
| − | === Generalized Random Forests - arXiv === | + | ===Generalized Random Forests - arXiv=== |
''[Submitted on 5 Oct 2016 (v1), last revised 5 Apr 2018 (this version, v4)]'' | ''[Submitted on 5 Oct 2016 (v1), last revised 5 Apr 2018 (this version, v4)]'' | ||
| − | === Machine Learning Methods Economists Should Know About === | + | ===Machine Learning Methods Economists Should Know About=== |
''[Submitted on 24 Mar 2019]'' | ''[Submitted on 24 Mar 2019]'' | ||
| − | == 编者推荐 == | + | ==编者推荐== |
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| + | 待补充 | ||
2022年7月7日 (四) 22:33的版本
因果森林 (Causal Forest) 是由 Wager and Athey [2017] 提出的异质因果效应估计算法。该算法本质上可以理解成通过子采样生成大量不同的因果树，然后取这些树的平均。 类似于随机森林算法，因果森林可以被理解成一种近邻匹配算法，但是用数据驱动的方法来确定样本之间的相似度。
介绍
待补充
$$\hat{\theta}(x) = \argmin_{\theta} \sum_{i=1}^n K_x(X_i)\cdot \left( Y_i - \hat{q}(X_i, W_i) - \theta \cdot (T_i - \hat{f}(X_i, W_i)) \right)^2$$
算法
待补充 ....
算法实现
Python 包 econml 和 R 包 grf 都有实现该算法。
参考文献
Recursive Partitioning for Heterogeneous Causal Effects - arXiv
[Submitted on 5 Apr 2015 (v1), last revised 30 Dec 2015 (this version, v3)]
Estimation and Inference of Heterogeneous Treatment Effects ...
[Submitted on 14 Oct 2015 (v1), last revised 10 Jul 2017 (this version, v4)]
Generalized Random Forests - arXiv
[Submitted on 5 Oct 2016 (v1), last revised 5 Apr 2018 (this version, v4)]
Machine Learning Methods Economists Should Know About
[Submitted on 24 Mar 2019]
编者推荐
待补充