We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. We develop a general framework based on knowledge of the multivariate observational data distribution. We then propose an algorithm for finite sample data, discuss its asymptotic properties, and illustrate its performance on simulated and real data.

翻译：我们提出一种方法，用于检测非线性因果加性及潜在异方差噪声模型中的设定错误。我们的目标是识别即使在存在此类设定错误的情况下，仍能推断其因果效应的预测变量。我们基于多变量观测数据分布知识，建立了一个通用框架。随后，我们针对有限样本数据提出了一种算法，讨论了其渐近性质，并在模拟数据与实际数据上展示了其性能。