[gov3009-l] Applied Statistics 10/26/16 - In Song Kim

Hi all, This week at the Applied Statistisc workshop we will be welcoming In Song Kim, an Assistant Professor of Political Science at MIT. He will be presenting work entitled "When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?" Please find the abstract below and on the website. The most recent version of the paper can be found here: http://web.mit.edu/insong/www/pdf/FEmatch.pdf We will meet in CGIS Knafel Room 354 at noon and lunch will be provided. Best, Pam Title: When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data? Abstract: Many social scientists use linear fixed effects regression models for causal inference with longitudinal data to account for unobserved time-invariant confounders. We show that these models require two additional causal assumptions, which are not necessary under an alternative selection-on-observables approach. Specifically, the models assume that past treatments do not directly influence current outcome, and past outcomes do not directly affect current treatment. The assumed absence of causal relationships between past outcomes and current treatment may also invalidate some applications of before-and-after and difference-in-differences designs. Furthermore, we propose a new matching framework to further understand and improve one-way and two-way fixed effects regression estimators by relaxing the linearity assumption. Our analysis highlights a key trade-off --- the ability of fixed effects regression models to adjust for unobserved time-invariant confounders comes at the expense of dynamic causal relationships between treatment and outcome.