A1244
Title: Identification of causal effects via instrumental variables from an auxiliary heterogeneous population
Authors: Wei Li - Renmin University of China (China) [presenting]
Abstract: Evaluating causal effects in a primary population of interest with unmeasured confounders is challenging. Although instrumental variables (IVs) are widely used to address unmeasured confounding, they may not always be available in the primary population. Fortunately, IVs might have been used in previous observational studies on similar causal problems, and these auxiliary studies can be useful to infer causal effects in the primary population, even if they represent different populations. However, existing methods often assume homogeneity or equality of conditional average treatment effects between the primary and auxiliary populations, which may be limited in practice. The aim is to remove the homogeneity requirement and establish a novel identifiability result, allowing for different conditional average treatment effects across populations. A multiply robust estimator that remains consistent despite partial misspecifications of the observed data model is also constructed and achieves local efficiency if all nuisance models are correct. The proposed approach is illustrated through simulation studies. The approach is finally applied by leveraging data from lower-income individuals with cigarette prices as a valid IV to evaluate the causal effect of smoking on physical functional status in higher-income groups where strong IVs are not available.
