Related papers: Nonlinear Causal Discovery via Kernel Anchor Regression

Nonlinear Causal Discovery via Kernel Anchor Regression

URL: http://arxiv.org/abs/2210.16775v1
Date: Sun, 30 Oct 2022 08:46:36 GMT
Title: Nonlinear Causal Discovery via Kernel Anchor Regression
Authors: Wenqi Shi and Wenkai Xu
Abstract summary: We tackle the nonlinear setting by proposing kernel anchor regression (KAR) We provide convergence results for the proposed KAR estimators and the identifiability conditions for KAR to learn the nonlinear structural equation models (SEM) Experimental results demonstrate the superior performances of the proposed KAR estimators over existing baselines.
Score: 12.672917592158269
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Learning causal relationships is a fundamental problem in science. Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear. In this work, we tackle the nonlinear setting by proposing kernel anchor regression (KAR). Beyond the natural formulation using a classic two-stage least square estimator, we also study an improved variant that involves nonparametric regression in three separate stages. We provide convergence results for the proposed KAR estimators and the identifiability conditions for KAR to learn the nonlinear structural equation models (SEM). Experimental results demonstrate the superior performances of the proposed KAR estimators over existing baselines.

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