Difference-in-differences (DID) is commonly used for causal inference in time-series cross-section data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, I propose a method that not...

We introduce trajectory balancing, a general reweighting approach to causal inference with time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is exposed to treatment at a given time, while a set of control units remain untreated throughout a time window of...

This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Its special cases include several newly developed methods, such as the fixed...