Consistency of Causal Inference under the Additive Noise Model
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):478-486, 2014.
We analyze a family of methods for statistical 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 family of inference methods consistently infers the causal direction in a nonparametric setting.