Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
错位实施双重差分中的机器学习与动态处理效应异质性
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning. The proposed method, machine learning difference-in-differences (MLDID), allows for estimation of time-varying conditional average treatment effects on the treated, which can be used to conduct detailed inference on drivers of treatment effect heterogeneity. We…