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.

翻译：本文提出了一个简单的框架,用时间序列跨部门数据对因果关系进行反事实估计,其中我们估计直接估算反事实结果对治疗结果对治疗的平均处理影响,我们在此框架下讨论若干新的估计因素,包括固定效果反事实估量器、交互固定效果反事实估测器和矩阵完成估计器。当治疗效果各不相同或没有观察到时间变化的混淆者存在时,它们比传统的双向固定效果模型提供更可靠的因果关系估计。此外,我们提出一个新的动态治疗效果图案,同时进行若干诊断性测试,以帮助研究人员衡量确定假设的有效性。我们用两个政治经济实例来说明这些方法,并在R和Stata中开发一个开放源的组合,即效果,以便利执行。