One of the common critiques of machine learning models is that they do not produce causal (or sometimes even stable) inferences. In this talk, I will try to lay out a distinction between causation and prediction. Using this distinction, I will illustrate how some very important problems--social and conceptual--can be best tackled with predictive--not causal--tools. I will illustrate using data from criminal justice, behavioral economics and finance.