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B1017
Title: Partial identification of causal effects in grouped data with unobserved confounding Authors:  Jennifer Hill - New York University (United States) [presenting]
Abstract: The unbiased estimation of a treatment effect in the context of observational studies with grouped data is considered. When analyzing such data, researchers typically include as many predictors as possible, in an attempt to satisfy ignorability, and so-called fixed effects (indicators for groups) to capture unobserved between-group variation. However, depending on the mathematical properties of the data generating process, adding such predictors can actually increase treatment effect bias if ignorability is not satisfied. Exploiting information contained in multilevel model estimates, we generate bounds on the comparative potential bias of competing methods, which can inform model selection. Our approach relies on a parametric model for grouped data and an omitted confounder, establishing a framework for sensitivity analysis. We characterize the strength of the confounding along with bias amplification using easily interpretable parameters and graphical displays. Additionally we provide estimates of the uncertainty in the derived bounds and create a framework for estimating causal effects with partial identification.