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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Bibliographic Details
Published in:Acta Mathematicae Applicatae Sinica 2020-07, Vol.36 (3), p.702-713
Main Authors: Wu, Peng, Hu, Qi-rui, Tong, Xing-wei, Wu, Min
Format: Article
Language:English
Subjects:
Applications of Mathematics
Machine learning
Matching
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Theoretical
Tropical storms
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Summary: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.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-020-0960-1