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
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