An Optimal Transport Approach to Estimating Causal Effects via Nonlinear Difference-in-Differences
用最优传输与非线性双重差分估计因果效应
We propose a nonlinear difference-in-differences method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data. Our approach sheds a new light on existing approaches like the changes-in-changes and the classical semiparametric difference-in-differences estimator and generalizes them to settings with multivariate heterogeneity in the outcomes. The main benefit of this extension is that it allows for…