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14 June 2023 Comparison of two causal inference methods: linear regression and matching score and introduction of causal forest
Zhiqi Huang, Weiwen Mo
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Proceedings Volume 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023); 1272509 (2023) https://doi.org/10.1117/12.2679249
Event: International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 2023, Suzhou, China
Abstract
Under the context of the rapid development of artificial intelligence, the introduction of causal inference will improve the accuracy of machine analysis of data. This work aims to introduce three methods of calculating causal effect, linear regression, propensity score matching and causal forest, illuminate some about the combination of causal inference and matching-learning. Here, we substitute the dataset National Supported Work experiment by Lalonde (1986) into three methods and compare the results. We show experimentally that causal forest Causal forests can minimize data bias and obtain more accurate estimates of causal effects.
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Zhiqi Huang and Weiwen Mo "Comparison of two causal inference methods: linear regression and matching score and introduction of causal forest", Proc. SPIE 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 1272509 (14 June 2023); https://doi.org/10.1117/12.2679249
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KEYWORDS
Linear regression
Decision trees
Random forests
Artificial intelligence
Evolutionary algorithms
Machine learning
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