Abstract
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.
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This paper is supported by the National Key Research and Development Program of China Grant 2017Y-FA0604903 and National Natural Science Foundation of China Grant (Nos. 11671338, 11971064).
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Wu, P., Hu, Qr., Tong, Xw. et al. Learning Causal Effect Using Machine Learning with Application to China’s Typhoon. Acta Math. Appl. Sin. Engl. Ser. 36, 702–713 (2020). https://doi.org/10.1007/s10255-020-0960-1
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DOI: https://doi.org/10.1007/s10255-020-0960-1