19/06/2026 - 14:30 - ISBA C.115 - 
 

Kris Sankaran 

(University of Wisconsin - Madison) 

Will give a presentation on : 

A Brief Introduction to Shapley Values  

Abstract: 

This workshop will introduce Shapley values, a game-theoretic concept adapted to explain predictions from black-box machine learning models. We study the method from statistical and geometric perspectives, then apply language from causal inference to discuss subtleties in its interpretation. Since naive implementations become computationally intractable as samples or features grow, we review approximation methods like IME and KernelSHAP. We close with practical implementations in R (shapviz) and Python (shap), motivated by interpretable machine learning problems from social science and developmental biology. Material will be accessible to researchers with experience in linear regression and supervised machine learning. The lecture is based off notes for a recently developed undergraduate course on interpretable machine learning (https://github.com/krisrs1128/stat479_notes).