论文标题
非线性因果通过内核锚回归发现
Nonlinear Causal Discovery via Kernel Anchor Regression
论文作者
论文摘要
学习因果关系是科学中的一个基本问题。尽管假定变量之间的关系是线性的，但已开发出锚回归是为了解决大量因果图形模型的问题。在这项工作中，我们通过提出内核锚回归（KAR）来解决非线性设置。除了使用经典的两阶段最小平方估计器的自然配方外，我们还研究了一种改进的变体，涉及三个单独的阶段的非参数回归。我们为拟议的KAR估计器和KAR学习非线性结构方程模型（SEM）的可识别性条件提供了收敛结果。实验结果表明，所提出的KAR估计器的表现优于现有基线。
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.
