Learning Causal Relationships from Conditional Moment Conditions by Importance Weighting

• URL: http://arxiv.org/abs/2108.01312v1
• Date: Tue, 3 Aug 2021 06:07:07 GMT
• Title: Learning Causal Relationships from Conditional Moment Conditions by Importance Weighting
• Authors: Masahiro Kato and Haruo Kakehi and Kenichiro McAlinn and Shota Yasui
• Abstract summary: We consider learning causal relationships under conditional moment conditions. conditional moment conditions pose serious challenges for causal inference in high-dimensional settings. We propose a method that transforms conditional moment conditions to unconditional moment conditions.
• Score: 11.326695040991192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider learning causal relationships under conditional moment conditions. Unlike causal inference under unconditional moment conditions, conditional moment conditions pose serious challenges for causal inference, especially in complex, high-dimensional settings. To address this issue, we propose a method that transforms conditional moment conditions to unconditional moment conditions through importance weighting using the conditional density ratio. Then, using this transformation, we propose a method that successfully approximates conditional moment conditions. Our proposed approach allows us to employ methods for estimating causal parameters from unconditional moment conditions, such as generalized method of moments, adequately in a straightforward manner. In experiments, we confirm that our proposed method performs well compared to existing methods.

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