Where does causal thinking meet machine learning? We will discuss several such cases. We first show how we use learning theory to guide us in building algorithms for inferring individual-level causal effects, and how we apply these ideas to create deep-learning causal-effect inference methods. We then show how ideas from causal inference can help us in two important machine learning tasks: learning robust classifiers and interpreting deep image recognition system. If time permits, we’ll discuss a recent application of machine learning for learning individualized treatments for patients in an acute hospital setting.