When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?

By Kosuke Imai, In Song Kim. American Journal of Political Science, 2019

342 citations recorded by the source corpus.

Many researchers use unit fixed effects regression models as their default methods for causal inference with longitudinal data. We show that the ability of these models to adjust for unobserved time‐invariant confounders comes at the expense of dynamic causal relationships, which are permitted under an alternative selection‐on‐observables approach. Using the nonparametric directed acyclic graph, we highlight two key causal identification assumptions of unit fixed effects models: Past treatments do not directly influence current outcome, and past outcomes do not affect current treatment. Furthermore, we introduce a new nonparametric matching framework that elucidates how various unit fixed effects models implicitly compare treated and control observations to draw causal inference. By establishing the equivalence between matching and weighted unit fixed effects estimators, this framework enables a diverse set of identification strategies to adjust for unobservables in the absence of dynamic causal relationships between treatment and outcome variables. We illustrate the proposed methodology through its application to the estimation of GATT membership effects on dyadic trade volume.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/361bcd4b7e6795a1ad03cb21c3835b66da42822f

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Citations in the corpus

Does Public Support Help Democracy Survive? (2019, background): “A higher level of support, 12I closely follow the example of Imai and Kim (2019), who use graphs to describe the identifi- cation of causal effects in a panel data context.”
Selection into Identification in Fixed Effects Models, with Application to Head Start (2019, neutral): “This will typically not be the case, as shown theoretically in Chernozhukov et al. (2013); Gibbons, Suárez, and Urbancic (2019); Imai and Kim (2019), and as we demonstrate in our empirical application below.”
Panel experiments and dynamic causal effects: A finite population perspective (2020, result): “1 is related to yet crucially different from results in Imai and Kim (2019), which show that the unit fixed effects estimator recover a weighted average of unit-specific contemporaneous causal effects if there are no carryover effects.”
How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, background): “Panel data have become particularly prominent for causal inference based on observational data (Bell and Jones 2015; Brüderl and Ludwig 2015; Imai and Kim 2019).”
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data (2020, methodology): “Indeed, by extending the matching framework of Imai and Kim (2019), we show that the simultaneous adjustment for the two types of unobserved confounders cannot be done nonparametrically under the 2FE framework.”