This paper introduces the concept of "prediction policy problems," arguing that not all policy decisions require causal inference; many benefit significantly from accurate predictions. The authors distinguish these from traditional "causal inference" problems through examples, such as deciding whether to take an umbrella (prediction) versus whether a rain dance causes rain (causal). They explain how machine learning (ML) excels in prediction by effectively managing the bias-variance trade-off and allowing for flexible models, unlike conventional methods like Ordinary Least Squares (OLS) that prioritize unbiasedness. An illustrative application in healthcare demonstrates how ML can identify and reduce "futile surgeries" by predicting patient mortality, leading to substantial savings and improved patient outcomes. The text concludes by highlighting the widespread applicability and importance of prediction problems across various policy domains, suggesting they warrant greater attention and reorientation in economic research.