{"id":3946,"date":"2026-01-10T13:55:26","date_gmt":"2026-01-10T21:55:26","guid":{"rendered":"https:\/\/colleges.claremont.edu\/ccms\/?post_type=tribe_events&p=3946"},"modified":"2026-01-27T08:40:06","modified_gmt":"2026-01-27T16:40:06","slug":"ccms-colloquium-teal-witter-cmc","status":"publish","type":"tribe_events","link":"https:\/\/colleges.claremont.edu\/ccms\/event\/ccms-colloquium-teal-witter-cmc\/","title":{"rendered":"CCMS Colloquium: Teal Witter (CMC)"},"content":{"rendered":"
CCMS Colloquium invites you to a talk by Teal Witter (CMC)<\/div>\n
<\/div>\n

<\/p>\n

Title: <\/strong> Exactly Computing do-Shapley Values<\/div>\n

<\/p>\n

\n

Abstract:\u00a0<\/strong><\/p>\n

Causal questions lie at the heart of scientific inquiry, from evaluating economic policies to determining medical treatments. Yet, observational data alone is often insufficient due to the fundamental problem of causal inference: we cannot observe the counterfactual world where a specific intervention did not occur. Structural Causal Models (SCMs) offer a powerful solution by explicitly modeling the underlying mechanisms of a system. By formalizing data generation, SCMs allow us to use the do-operator to rigorously simulate interventions, answering questions like, “If a patient were administered prednisone and made to stop smoking, what would be their expected pain level?”<\/div>\n
<\/div>\n
However, characterizing a system through individual queries is computationally daunting. As the number of features\u00a0d<\/i>\u00a0grows, the landscape of possible interventions scales exponentially (2^d<\/i>). To extract interpretable insights from this combinatorial complexity, we utilize the do-Shapley value, a game-theoretic framework that attributes the complicated\u00a0dynamics of an SCM to individual features.<\/div>\n
<\/div>\n
In this talk, I will present a new algorithmic approach that makes computing these values more tractable. We show that the causal landscape is structured into “irreducible sets”, a building block where multiple interventions yield identical effects. By leveraging this structure, we introduce an algorithm that computes do-Shapley values exactly, with runtime that depends on the graph’s complexity rather than\u00a02^d.<\/i>\u00a0We further propose an estimator that targets these sets directly, producing more accurate estimates than prior work by several orders of magnitude or more.<\/div>\n
<\/div>\n
Joint work\u00a0with \u00c1lvaro Parafita, Tomas Garriga, Maximilian Muschalik, Fabian Fumagalli, Axel Brando, and Lucas Rosenblatt.<\/div>\n<\/div>\n

<\/p>\n

Bio:\u00a0<\/strong>Teal is an Assistant Professor of Mathematics and Computer Science at Claremont McKenna College. His recent research explores randomized algorithms for problems in explainable AI and generative AI. More broadly, he is interested in leveraging ideas from theoretical computer science and machine learning to design provably accurate algorithms. Before joining the consortium, Teal completed his Ph.D. in Computer Science at New York University, where he was supported by an NSF Graduate Research Fellowship. Prior to graduate school, Teal attended Middlebury College.<\/span><\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

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