  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.
