Correcting Confounding via Random Selection of Background Variables

• URL: http://arxiv.org/abs/2202.02150v1
• Date: Fri, 4 Feb 2022 14:27:10 GMT
• Title: Correcting Confounding via Random Selection of Background Variables
• Authors: You-Lin Chen, Lenon Minorics, Dominik Janzing
• Abstract summary: We propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y. We prove, subject to a symmetry assumption for the background influence, that V converges to zero if and only if X contains no causal drivers.
• Score: 15.206717158865022
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features. To this end, we propose a statistic V measuring the coefficient's variability. We prove, subject to a symmetry assumption for the background influence, that V converges to zero if and only if X contains no causal drivers. In experiments with simulated data, the method outperforms state of the art algorithms. Further, we report encouraging results for real-world data. Our approach aligns with the general belief that causal insights admit better generalization of statistical associations across environments, and justifies similar existing heuristic approaches from the literature.

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