With the rapid accumulation of high variety high volume data, there is an increasing demand for computational methods that can perform effective and systematic knowledge discovery. In this talk, I will give an overview of computational causal discovery methods. These methods aim to discover the underlying data generation processes, i.e. causal mechanisms, from observational data, experimental data, and the combination of the two. The operating principles and the general analytical frameworks of computational causal discovery methods will be introduced, along with examples of their applications in biomedical sciences. I will also discuss causal feature selection, one important intersection of computational causal discovery and supervised learning predictive modeling. Causal feature selection is a class of feature selection methods that utilizes the causal relationships among the outcome and the predictors for feature selection. Causal feature selection methods produce predictor sets that result in robust, parsimonious, and interpretable predictive models.