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

Ban, Pamela pban at fas.harvard.edu
Tue Oct 25 10:18:01 EDT 2016

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.


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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