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 and we then propose an algorithm for finite sample data, discuss its asymptotic properties, and illustrate its performance on simulated and real data.

翻译：我们提出一种方法来检测非线性因果加性（可能含异方差噪声）模型中的模型误设。我们旨在识别即使在存在此类误设的情况下仍能推断因果效应的预测变量。基于对多变量观测数据分布的认识，我们建立了一个通用框架，随后针对有限样本数据提出一种算法，讨论其渐近性质，并在模拟数据和真实数据上展示其性能。