Identification and Auto-Debiased Machine Learning for Outcome-Conditioned Average Structural Derivatives
结果条件平均结构导数的识别与自动去偏机器学习
This study proposes a new class of heterogeneous causal quantities, referred to as outcome-conditioned average structural derivatives (OASDs), in a general nonseparable model. An OASD is the average partial effect of a marginal change in a continuous treatment on individuals located on different parts of an outcome distribution, irrespective of individuals’ characteristics. We show that OASDs extend the unconditional quantile partial effects (UQPE) proposed by Firpo, Fortin,…