Learning causality by identifying common effects with kernel-based dependence measures
Xiaohai Sun, Dominik Janzing · 2007
Abstract. We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernelbased conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y. Based on this assumption, we collect “votes ” for hypothetical causal directions and orient the edges according to the majority vote. For most of our experiments with artificial and real-world data our method has outperformed the conventional constraint-based inductive causation (IC) algorithm. 1