We introduce and defend the "prediction policy problems" (PPP) framework, which posits that many public policy and economic challenges have an often-overlooked predictive element that machine learning (ML) can significantly enhance. The document addresses key criticisms, arguing that the framework doesn't seek to replace causal inference but rather to improve the predictive "bricks" within complex policy decisions, which inherently include prediction, causal inference, and normative judgment. It emphasizes that accurate prediction is crucial for efficient and equitable resource allocation and that the framework has spurred the development of causal ML methods that integrate prediction with causal analysis. Furthermore, the text contends that challenges like target-construct mismatch and dynamic systems are inherent to quantitative policy analysis and that the PPP framework offers a more transparent and adaptable approach than traditional methods. Finally, it stresses that responsible implementation requires a robust "institutional wrapper" encompassing transparency, human oversight, and contestability, asserting that the proper comparison for algorithmic systems is not perfection, but the often-flawed human-centric status quo.