Score-based causal feature selection for cancer risk prediction

The primary goal of cancer risk prediction is to find dominant features, with each being responsible for cancer diagnosis. As a result, selected feature sets often converge to inexplicable and implausible results. Existing score-based causal models adopt a score function to learn a local causal structure to explicitly characterize a unique causal configuration as a variable number of nodes and links, which however suffer from nonconvex optimization and global incompleteness. This leads us to present a score-based approach to construct a causal network by optimizing a score function with a convex solution under the constraint of causal Markov property. It can be analytically shown that the resulting causal network satisfies the causal Markov property, and as a result, all cause-effect dependencies can be retained and are globally consistent. An additional node selector is introduced to choose the most dominant causal features. Empirical evaluations on three benchmarks and one in-house cancer risk datasets suggest our approach significantly outperforms the state-of-the-arts.