Causality in Econometrics: Choice vs Chance
梳理了统计学与计量经济学中两种因果推断方法的演变与近期融合，指出局部平均处理效应框架通过透明化关键假设促进了跨学科共识，并展望了兼顾透明性与相关性的新进展。
This essay describes the evolution and recent convergence of two methodological approaches to causal inference. The first one, in statistics, started with the analysis and design of randomized experiments. The second, in econometrics, focused on settings with economic agents making optimal choices. I argue that the local average treatment effects framework facilitated the recent convergence by making key assumptions transparent and intelligible to scholars in many fields. Looking ahead, I discuss recent developments in causal inference that combine the same transparency and relevance.