Consistency of causal inference under the additive noise model. ICML

We analyze a family of methods for statisti-cal causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given fam-ily of inference methods consistently infers the causal direction in a nonparametric setting. 1