Kara Rudolph, Columbia University, Causal Mediation Workshop

Biography: Kara Rudolph is an epidemiologist interested in developing and applying causal inference methods to better understand the prevention and treatment of substance use disorders. Currently, she is a Associate Professor of Epidemiology at Columbia University. Her current work focuses on developing and applying methods for transportability and mediation to understand mechanisms relevant for drug use disorder prevention and treatment in various target populations. More generally, her work on generalizing/ transporting findings from study samples to target populations and identifying subpopulations most likely to benefit from interventions contributes to efforts to optimally target available policy and program resources. She has completed a PhD in Epidemiology and an MHS in Biostatistics from the Johns Hopkins Bloomberg School of Public Health and was a Robert Wood Johnson Foundation Health and Society Scholar.

Causal Mediation Workshop
Causal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome, a central goal in epidemiology and health sciences. However, rapid methodologic developments coupled with few formal courses presents challenges to implementation. Beginning with an overview of classical direct and indirect effects, this workshop will present recent advances that overcome limitations of previous methods, allowing for: (i) continuous exposures, (ii) multiple, non-independent mediators, and (iii) effects identifiable in the presence of intermediate confounders affected by exposure. Emphasis will be placed on flexible, stochastic and interventional direct and indirect effects, highlighting how these may be applied to answer substantive epidemiological questions from real-world studies. Multiply robust, nonparametric estimators of these causal effects, and free and open source R packages (crumble) for their application, will be introduced. To aid translation to real-world data analysis, this workshop will incorporate hands-on R programming exercises to allow participants practice in implementing the statistical tools presented. It is recommended that participants have working knowledge of the basic notions of causal inference, including counterfactuals and identification (linking the causal effect to a parameter estimable from the observed data distribution). Familiarity with the R programming language is also recommended.