A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models
提出一种新的非负结果变量平均处理效应分解方法，将总效应表示为受处理诱导参与组和始终参与组效应的加权和，并指出文献中常用的广延与集约边际分解通常缺乏因果解释，两种分解可能得出截然相反的结论。
This note proposes a new decomposition of average treatment effects on nonnegative outcomes. It represents the total effect as a population-weighted sum of the effects for two groups: those induced to participate by the treatment and those participating regardless of it. The usual decomposition into extensive and intensive margins used in the literature is generally incompatible with such a causal interpretation. The difference between decompositions can be substantial and yield diametrically opposed results. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.