[gov3009-l] Applied Stats 4/18

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.