Methods for computational causal discovery in biomedicine

With the development of high throughput technology in the past twenty years, it has become easier and cheaper to simultaneously measure tens of thousands of molecules in biological systems. One of the major challenges is how to extract knowledge from these high dimensional datasets and infer the underlying mechanisms of the system. In this review, we discuss several topics related to causal discovery from biomedical data, including causal structural learning from observational and experimental data, estimation of causal effects, and using causal information for predictive modeling.