Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
利用随机森林推广内生缺失结果数据的修剪界限
In many experimental or quasi-experimental studies, outcomes of interest are only observed for subjects who select (or are selected) to engage in the activity generating the outcome. Outcome data is thus endogenously missing for units who do not engage, in which case random or conditionally random treatment assignment prior to such choices is insufficient to point identify treatment effects. Non-parametric partial identification bounds are a way to address endogenous…