Learning Causal Models from Conditional Moment Restrictions by Importance Weighting

Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Haruo Kakehi and Shota Yasui

Papers from arXiv.org

Abstract: We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions pose serious challenges for causal inference, especially in high-dimensional settings. To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting, using a conditional density ratio estimator. Using this transformation, we successfully estimate nonparametric functions defined under conditional moment restrictions. Our proposed framework is general and can be applied to a wide range of methods, including neural networks. We analyze the estimation error, providing theoretical support for our proposed method. In experiments, we confirm the soundness of our proposed method.

Date: 2021-08, Revised 2022-09
New Economics Papers: this item is included in nep-ecm and nep-isf
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