A Practical Guide to Counterfactual Estimators for Causal Inference with Time‐Series Cross‐Sectional Data
A Practical Guide to Counterfactual Estimators for Causal Inference with Time‐Series Cross‐Sectional Data
Abstract 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, …