因果森林 Causal Forest

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。

算法

....

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]