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Your Environment. Your Health.

Bayesian Subset Selection and Variable Importance for Interpretable Prediction and Classification

Powering Research Through Innovative Methods for Mixtures in Epidemiology (PRIME)

June 4, 2021
Virtual Meeting

14 people sitting at circular table with 1s and 0s as background, seen from above


Subset selection is a valuable tool for interpretable learning, scientific discovery, and data compression. However, classical subset selection is often eschewed due to selection instability, computational bottlenecks, and lack of post-selection inference.

Our analysis provides unique insights into the combination of environmental, socioeconomic, and demographic factors that predict educational outcomes, and features highly competitive prediction with remarkable stability.


Daniel Kowal, Ph.D.

Daniel Kowal, Ph.D., completed his Ph.D. in Statistics at Cornell University in 2017. After completing his Ph.D., Kowal joined the Department of Statistics at Rice University as an Assistant Professor in July 2017.

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