Hi all,
Our next virtual meeting will be at 12pm (EST) Wednesday, February 17
(tomorrow), where I (Soichiro Yamauchi) will present "Adjusting for
Unmeasured Confounding in Marginal Structural Models with Propensity-Score
Fixed Effects." This is joint work with Matthew Blackwell (Harvard
University).
*Abstract*:
Marginal structural models are a popular tool for investigating the effects
of time-varying treatments, but they require the assumption that there are
no unobserved confounders between the treatment and outcome. With
observational data, this assumption may be difficult to maintain, and in
studies with panel data, many researchers use fixed effects models to purge
the data of time-constant unmeasured confounding. Unfortunately,
traditional linear fixed effects models are not suitable for marginal
structural models, since they can only estimate lagged effects under
implausible assumptions. To resolve this tension, we propose a novel
inverse probability of treatment weighting estimator with propensity-score
fixed effects to adjust for time-constant unmeasured confounding in
marginal structural models. We show that, in spite of the incidental
parameters problem, these estimators are consistent and asymptotically
normal when the number of units and time periods grow at a similar rate.
Unlike traditional fixed effect models, this approach works even when the
outcome is only measured at a single point in time as is common in marginal
structural models. We apply these methods to estimate the effect of
negative advertising on the electoral success of candidates for statewide
offices in the United States.
*Zoom link: *
https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09
*Schedule of the workshop:*
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all tomorrow!
Best,
Soichiro
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/