Structural Causal Models and the Specification of Time-Series-Cross-Section Models

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The structural causal models (SCM) of Pearl (1995, 2000, 2009) provide a graphical criterion for choosing the “right hand side” variables to include in a model. In this paper, we use SCMs to address the question of whether to include lagged variables in time-series-cross-section (TSCS) models. This question has received a great deal of attention from political methodologists, but unfortunately, the practical advice for applied researchers that comes out of this literature varies considerably from article to article. We attempt to clarify the nature of some of these disagreements and to provide useful tools to reason about the nonparametric identification of causal effects. After clarifying the debate between Beck and Katz (1996, 2011) and Achen (2000) and adding to the discussion by Keele and Kelly (2006), we provide concrete nonparametric identification results for commonly studied TSCS data generating processes. These results are also relevant for the choice of control variables in cross-section (CS) models. We conclude with some general thoughts on how a focus on using the SCM as a tool for proving identification results can help TSCS and CS researchers do better work.

Last updated on 04/03/2013