Prediction Policy Problems
指出有一类政策问题不需要因果推断，而需要预测推断，并论证机器学习方法比传统回归更适合解决这类问题，通过健康政策案例展示改进预测能带来巨大的社会福利增益。
Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.