Replication Data for: A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data

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.

本文提出一种面向时间序列截面数据（time-series cross-sectional data）因果推断（causal inference）的简洁反事实估计（counterfactual estimation）框架：通过直接为已接受处理的观测样本估算反事实结果，即可得到处理组平均处理效应（average treatment effect on the treated）。本文讨论了该框架下的数种新型估计器，包括固定效应反事实估计器、交互固定效应反事实估计器与矩阵补全估计器。当处理效应存在异质性或存在未观测到时变混杂因素时，上述估计器相较于传统双向固定效应模型，能够给出更可靠的因果推断结果。此外，本文还提出了一种全新的动态处理效应绘图法，搭配数种诊断检验工具，以帮助研究者评估识别假设的合理性。本文通过两个政治经济学案例对上述方法进行了演示，并开发了适用于R与Stata的开源工具包fect，以方便方法的落地实现。