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添加210字节 、 2022年7月7日 (四) 22:33
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== 算法 ==
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$$\hat{\theta}(x) = \argmin_{\theta} \sum_{i=1}^n K_x(X_i)\cdot \left( Y_i - \hat{q}(X_i, W_i) - \theta \cdot (T_i - \hat{f}(X_i, W_i)) \right)^2$$
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==算法==
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[[文件:广义随机森林.png|缩略图]]
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== 算法实现 ==
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==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。
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== 参考文献 ==
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==参考文献==
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=== Recursive Partitioning for Heterogeneous Causal Effects - arXiv ===
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===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)]''
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=== Estimation and Inference of Heterogeneous Treatment Effects ... ===
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===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)]''
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=== Generalized Random Forests - arXiv ===
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===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)]''
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=== Machine Learning Methods Economists Should Know About ===
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===Machine Learning Methods Economists Should Know About===
 
''[Submitted on 24 Mar 2019]''
 
''[Submitted on 24 Mar 2019]''
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== 编者推荐 ==
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==编者推荐==
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