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

By Licheng Liu, Ye Wang, Yiqing Xu. Social Science Research Network, 2020

350 citations recorded by the source corpus.

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 effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. These estimators provide more reliable causal estimates than conventional two-way fixed effects models when the treatment effects are heterogeneous or unobserved time-varying confounders exist. Under this framework, we propose two sets of diagnostic tests, tests for (no) pre-trend and placebo tests, accompanied by visualization tools, to help researchers gauge the validity of the no-time-varying-confounder assumption. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d0c672511b7ce6a9460e4d61f874683b6a9e241e

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Citations in the corpus

What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, positive): “Borusyak et al. (2021) introduce a related approach which they refer to as an imputation estimator (see, also, Gardner, 2021, Liu et al., 2022 and Wooldridge, 2021 for similar proposals).”
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, methodology): “We start by reviewing Borusyak et al. (2021), before discussing the connection between their results and those in Gardner (2021) and Liu et al. (2021).”
Revisiting event study designs: robust and efficient estimation (2021, methodology): “More recent papers (e.g. de Chaisemartin and D’Haultfœuille, 2020; Liu et al., 2020) replace it with a placebo strategy: pretend that treatment happened k periods earlier for all eventually treated units, and estimate the average effects h = 0, .”