The paper discusses recent developments in econometrics relevant to policy evaluation, focusing on three areas: identification strategies, supplementary analyses, and machine learning methods for causal effects. It emphasizes the importance of distinguishing correlation from causality in observational studies and highlights methods such as synthetic control, regression discontinuity, and network analysis. The authors also discuss the use of machine learning to adjust for differences between treated and control units and to estimate heterogeneous treatment effects. They stress the need for supplementary analyses, including placebo and sensitivity analyses, to enhance the credibility of policy evaluations. The paper also addresses the limitations of randomized controlled experiments and the challenges of estimating causal effects from observational data, particularly in the context of the minimum wage and educational outcomes. It reviews recent advances in regression discontinuity designs, synthetic control methods, and nonlinear difference-in-differences models, emphasizing the importance of robustness and external validity in policy analysis. The authors conclude that these methods are essential for empirical researchers seeking to evaluate the causal effects of policies.The paper discusses recent developments in econometrics relevant to policy evaluation, focusing on three areas: identification strategies, supplementary analyses, and machine learning methods for causal effects. It emphasizes the importance of distinguishing correlation from causality in observational studies and highlights methods such as synthetic control, regression discontinuity, and network analysis. The authors also discuss the use of machine learning to adjust for differences between treated and control units and to estimate heterogeneous treatment effects. They stress the need for supplementary analyses, including placebo and sensitivity analyses, to enhance the credibility of policy evaluations. The paper also addresses the limitations of randomized controlled experiments and the challenges of estimating causal effects from observational data, particularly in the context of the minimum wage and educational outcomes. It reviews recent advances in regression discontinuity designs, synthetic control methods, and nonlinear difference-in-differences models, emphasizing the importance of robustness and external validity in policy analysis. The authors conclude that these methods are essential for empirical researchers seeking to evaluate the causal effects of policies.