Predicting cancer risks by a constraint-based causal network

A key challenge in cancer risk prediction is selecting representative features, with each being responsible for cancer diagnosis. This lead us to define a constraint-based approach that employs causal Markov property to discover local causal dependencies between features and cancer risk types. Our approach introduces a causal network generated from an identified network skeleton to explicitly characterize these unique causal configurations of a particular cancer risk as a variable number of nodes and links. It can be analytically shown that the resulting causal network satisfies the causal Markov property, and as a result, all local cause-effect dependencies can be retained and are globally consistent. An additional node selection estimator is introduced to choose the most representative features. Empirical evaluations on four cancer risk datasets suggest our approach significantly outperforms the state-of-the-art methods.