Imai, Kosuke and In Song Kim. ``When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?'' American Journal of Political Science, Forthcoming.

 

  Abstract

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. The open-source software is available for implementing the proposed methodology. (Last revised, September 2018)
Longer and earlier version with the results on two-way fixed effects.

  Software and Related Research

Kim, In Song and Kosuke Imai. ``wfe: Weighted Linear Fixed Effects Estimators for Causal Inference.'' available through The Comprehensive R Archive Network. 2011-2016.
Imai, Kosuke, In Song Kim, and Erik Wang. ``Matching Methods for Causal Inference with Time-Series Cross-Section Data..''

© Kosuke Imai
 Last modified: Thu Nov 15 21:41:29 EST 2018