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February 6, 2024

Robustly estimating heterogeneity in factorial data using Rashomon Partitions

12 to 1 p.m.
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Speaker: Tyler Harris McCormick (he/him/his) is a Professor of Statistics and Sociology at the University of Washington, where he is also a core faculty member in the Center for Statistics and the Social Sciences.  He is also a Senior Data Science Fellow at the eScience Institute, UW's data science center.  Tyler's work develops statistical models that infer dependence structure in scientific settings where data are sparsely observed or observed subject to error.  His recent projects include estimating features of social networks (e.g. the degree of clustering or how central an individual is) using data from standard surveys, inferring a likely cause of death (when deaths happen outside of hospitals) using reports from surviving caretakers, and quantifying & communicating uncertainty in predictive models for global health policymakers.  He holds a Ph.D. in Statistics (with distinction) from Columbia University and is the recipient of an NIH New Innovator (DP2) Award, NIH Career Development (K01) Award, Army Research Office Young Investigator Program Award, and a Google Faculty Research Award.  Tyler is the former Editor of the Journal of Computational and Graphical Statistics (JCGS) and a Fellow of the American Statistical Association.  More information is available on his website: thmccormick.github.io.

This presentation will propose an approach to enumerating heterogeneity in the relationship between an outcome and discrete covariates by creating a Rashomon Partitions Set (RPS). Each Rashomon partition consists of the feature combinations that maximize heterogeneity in the outcome space. We construct this by pooling similar feature combinations using priors over pooling patterns in an overarching Bayesian model.  We show that we can characterize the set of Rashomon Partitions in terms of its fraction of the overall posterior and size.  Further, we demonstrate that the RPS is enumerable in meaningful settings by leveraging the insight that many potential combinations of features are, in practice, nonsensical for pooling because they represent different dimensions in the covariate space.  We demonstrate RPS construction in the context of two practical settings: finding heterogeneity in outcomes of a randomized trial and examining racial disparities in health outcomes in a large clinical dataset.  This is joint work with Arun Chandrasekhar (Stanford Economics) and Aparajithan Venkateswaran (UW Statistics).