[gov3009-l] Applied Statistics 10/26/16 - In Song Kim
pban at fas.harvard.edu
Tue Oct 25 10:18:01 EDT 2016
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.
When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?
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.
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