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：我们提出一种检测非线性因果加性（可能异方差）噪声模型中模型误设的方法。旨在识别即使存在此类误设时仍能推断因果效应的预测变量。我们基于多变量观测数据分布知识构建了通用框架，进而针对有限样本数据提出相应算法，讨论其渐近性质，并通过模拟数据与真实数据展示其性能表现。