  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 directly imputing counterfactual
outcomes for treated observations. We discuss several novel estimators under
this framework, including the fixed effects counterfactual estimator,
interactive fixed effects counterfactual estimator, and matrix completion
estimator. They provide more reliable causal estimates than conventional twoway
fixed effects models when treatment effects are heterogeneous or unobserved
time-varying confounders exist. Moreover, we propose a new dynamic treatment
effects plot, along with several diagnostic tests, to help researchers gauge
the validity of the identifying assumptions. We illustrate these methods with
two political economy examples and develop an open-source package, fect, in
both R and Stata to facilitate implementation.
