Prediction Policy Problems.

Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual–what happens with and without a policy–this tight link of causality and policy seems natural. While this link holds in many cases, we argue that there are also many policy applications where causal inference is not central, or even necessary. Consider two toy examples. One policy maker facing a drought must decide whether to invest in a rain dance to increase the chance of rain. Another seeing clouds must deciding whether to take an umbrella to work to avoid getting wet on the way home? Both decisions could benefit from an empirical duty of rain. But each has different requirements of the estimator. One requires causality: do rain dances cause rain? The other does not, needing only prediction: is the chance of rain high enough to merit an umbrella? We often focus on rain dance like policy problems. But there are also many umbrella-like policy problems. Not only are these prediction problems neglected, machine learning can help us solve them more effectively. In this paper, we (i) provide a simple