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第4行: |
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| + | 待补充 |
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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]] |
| + | 待补充 |
| .... | | .... |
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− | == 算法实现 == | + | ==算法实现== |
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| 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 == | + | ==参考文献== |
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− | === 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)]'' |
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− | === 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)]'' |
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− | === 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)]'' |
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− | === Machine Learning Methods Economists Should Know About === | + | ===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 == | + | ==编者推荐== |
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| + | 待补充 |