A0704
Title: Estimating average treatment effects when treatment data are absent in a target study
Authors: Lan Wen - University of Waterloo (Canada) [presenting]
Abstract: Researchers are often interested in understanding the causal effect of treatment interventions. However, in some cases, the treatment of interest-readily available in a randomized controlled trial-is either not directly measured or entirely unavailable in observational datasets. This challenge has motivated the development of stochastic incremental propensity score interventions, which operate on post-intervention exposures affected by the treatment of interest, with the aim of approximating the causal effects of the treatment intervention. Yet, a key challenge lies in the fact that the precise distributional shift of these post-intervention exposures induced by the treatment is typically unknown, making it uncertain whether the approximation truly reflects the causal effect of interest. The primary objective is to explore data integration methodologies to characterize a distribution of post-intervention exposures resulting from the treatment in an external dataset, and to use this information to estimate counterfactual mean outcomes under treatment interventions, in settings where the observational data lack treatment information and the external data may not contain measurements of the outcome of interest. The underlying assumptions required for this approach are discussed, and methodological guidance on estimation strategies is provided to address these challenges.
