BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20261016T194940EDT-45519FrTkt@132.216.98.100 DTSTAMP:20261016T234940Z DESCRIPTION:Kara Rudolph\, PhD\n\nAssociate Professor of Epidemiology - Col umbia University\n\nWHEN: Wednesday\, September 30\, 2026\, from 3:30 to 4 :30 p.m.\n WHERE: Hybrid | 2001 McGill College Avenue\, Room 1140\; Zoom\n N OTE: Kara Rudolph will be presenting in person\n\nAbstract\n\nThe effect o f a treatment or exposure on an outcome may differ from one sample or popu lation to another. Such treatment effect heterogeneity drives concerns abo ut external validity—specifically\, in the presence of such treatment effe ct heterogeneity\, a treatment effect estimated in a study sample may not apply to a target population. There can be multiple drivers of such hetero geneity\, which we review. We focus on two scenarios here. First\, the sim plest scenario where the only source of heterogeneity is the differential distribution of effect modifiers across populations\, and second\, where t he sources of heterogeneity also include differences in the distribution o f mediating variables. Even semiparametric efficient estimators of transpo rted average effects can have large variability and wide confidence interv als in scenarios where there is limited overlap between the distribution o f these variables across populations. This motivates the need to develop t ransport estimators with improved precision. In this paper\, we show that the precision of transported estimates may be improved by considering two types of covariate dimension reduction\; we develop an estimator for each approach. First\, we propose a so-called “collaborative” estimator that re duces the dimension of the transport problem to improve efficiency. Second \, we construct two other semiparametric estimators for cases where the ca usal structure underlying effect heterogeneity is known.\n\nSpeaker Bio\n \nI am an epidemiologist with research interests in developing and applyin g causal inference methods to better understand how to prevent and treat s ubstance use disorders and prevent violence. My current work focuses on un derstanding issues related to data support and complex exposures and devel oping and applying methods for transportability and mediation to understan d mechanisms relevant for drug use disorder prevention and treatment in va rious target populations. More generally\, my work on generalizing/ transp orting findings from study samples to target populations and identifying s ubpopulations most likely to benefit from interventions contributes to eff orts to optimally target available policy and program resources. I complet ed a PhD in Epidemiology and an MHS in Biostatistics from the Johns Hopkin s Bloomberg School of Public Health and was a Robert Wood Johnson Foundati on Health and Society Scholar.\n\n \n DTSTART:20260930T193000Z DTEND:20260930T203000Z SUMMARY:Improving Efficiency in Estimating Transported Average Treatment Ef fects URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/improving-efficie ncy-estimating-transported-average-treatment-effects-374230 END:VEVENT END:VCALENDAR