Applied Stats Workshop (Gov 3009)

The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. Free lunch is provided. Xiang Zhou presents "Two residual-based methods to adjust for treatment-induced confounding in causal inference" Two residual-based methods to adjust for treatment-induced confounding in causal inference 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.