In the disciplines of medicine, biology and social science, causal mediation analysis is widely used to examine the effect of an exposure on an outcome through different causal pathways. Recently, the
This paper introduces a simple framework of counterfactual estimation for causal inference with time-series cross-sectional data, in which we estimate the average treatment effect on the treated by di
We develop a new approach for estimating average treatment effects in the observational studies with unobserved cluster-level heterogeneity. The previous approach relied heavily on linear fixed effect
