Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Xiang
Zhou*, Professor of Government at Harvard University. He will be presenting
work entitled *Two residual-based methods to adjust for treatment-induced
confounding in causal inference*. Please find the abstract below and on
the Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.
As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided. See you all there!
-- Dana Higgins
*Title:* *Two residual-based methods to adjust for treatment-induced
confounding in causal inference *
*Abstract:* Treatment-induced confounding arises in both causal inference
of time-varying treatments and causal mediation analysis where
post-treatment variables affect both the mediator and outcome. Existing
methods to adjust for treatment-induced confounding include, among others,
Robins's structural nest mean model (SNMM) with its g-estimation and
marginal structural models (MSM) with inverse probability weighting (IPW).
In this talk, I describe two alternative methods, one called
"regression-with-residuals" (RWR) and the other called "residual
balancing," for estimating the marginal means of potential outcomes. The
RWR method is a simple extension of Almirall et al.'s (2010) two-stage
estimator for studying effect moderation to the estimation of marginal
effects. In special cases, it is equivalent to Vansteelandt's (2009)
sequential g-estimator for estimating controlled direct effects. The
residual balancing method, on the other hand, can be considered a
generalization of Hainmueller's (2012) entropy balancing method to
time-varying settings. Numeric simulations show that the residual balancing
method tends to be more efficient and more robust than IPW in a variety of
settings.
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