论文标题
通过随机选择背景变量纠正混淆
Correcting Confounding via Random Selection of Background Variables
论文作者
论文摘要
我们提出了一种区分因果影响与以下情况下隐藏混杂的方法：给定目标变量，潜在的因果驱动器X和大量背景特征，我们提出了一个新的标准，用于识别因X上回归系数的稳定性，以选择Y的回归系数的稳定性，以选择不同的背景特征。为此，我们提出了一个统计v测量系数的可变性。我们证明，要受到背景影响的对称性假设，当and x不包含因果驱动因素时，V会收敛至零。在使用模拟数据的实验中，该方法的表现优于艺术算法的状态。此外，我们报告了真实数据的令人鼓舞的结果。我们的方法与一般的信念相吻合，即因果见解可以更好地概括跨环境的统计关联，并证明了文献中类似的现有启发式方法是合理的。
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.
