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Learning Causal Effect Using Machine Learning with Application to China’s Typhoon
Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data. Most of the matching literatures involve the estimating of propensity score with parametric model, which heavily depends on the model specification. In thi...
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Published in: | Acta Mathematicae Applicatae Sinica 2020-07, Vol.36 (3), p.702-713 |
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creator | Wu, Peng Hu, Qi-rui Tong, Xing-wei Wu, Min |
description | Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data. Most of the matching literatures involve the estimating of propensity score with parametric model, which heavily depends on the model specification. In this paper, we employ machine learning and matching techniques to learn the average causal effect. By comparing a variety of machine learning methods in terms of propensity score under extensive scenarios, we find that the ensemble methods, especially generalized random forests, perform favorably with others. We apply all the methods to the data of tropical storms that occurred on the mainland of China since 1949. |
doi_str_mv | 10.1007/s10255-020-0960-1 |
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subjects | Applications of Mathematics Machine learning Matching Math Applications in Computer Science Mathematical and Computational Physics Mathematics Mathematics and Statistics Theoretical Tropical storms |
title | Learning Causal Effect Using Machine Learning with Application to China’s Typhoon |
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