Causal Inference with Time-Series Cross-Sectional Data: A Reflection

34 Pages Posted: 10 Jan 2022 Last revised: 3 Apr 2023

Date Written: April 2, 2023


This chapter surveys new development in causal inference using time-series cross-sectional (TSCS) data. I start by clarifying two identification regimes for TSCS analysis: one under the strict exogeneity assumption and one under the sequential ignorability assumption. I then review three most commonly used methods by political scientists: the difference-in-differences approach, two-way fixed effects models, and the synthetic control method. For each method, I examine its assumptions, explain its pros and cons, and discuss its extensions. I then introduce several new methods under strict exogeneity or sequential ignorability, including the factor-augmented approach, PanelMatch, and marginal structural models. Finally, I conclude by providing some recommendations to applied researchers and pointing out several directions for future research.

Keywords: causal inference, time-series-cross-sectional data, panel data, difference-in-differences, two-way fixed effects, synthetic control

Suggested Citation

Xu, Yiqing, Causal Inference with Time-Series Cross-Sectional Data: A Reflection (April 2, 2023). Available at SSRN: or

Yiqing Xu (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States


Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics