Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Sophia Gunluk, le 12 décembre à 15h (mode hybride).

Titre :Causal Modeling for Real World Distribution Shifts

Date: vendredi 12 décembre à 15h

Location: Auditorium 1, MILA, 2e étage

Jury

Résumé

Machine learning systems are increasingly used in decision-making settingswhere predictions influence, and are influenced by, human behavior andobservational constraints. Historical data often reflect structuralinequities, policy choices, and may contain spurious or unstableassociations that distort the relationships a model attempts to learn. Oncedeployed, these systems can shape the behavior of individuals andinstitutions, altering the distributions on which future decisions aremade. Understanding these dynamics requires tools that connect causalstructure, distribution shifts, and the effects of strategic andpolicy-driven feedback.

First, causal modeling provides a way to distinguish stable mechanisms fromspurious regularities, clarifying which aspects of the data-generatingprocess remain invariant across environments. This perspective explainswhen classifiers degrade under strategic adaptation, how feedback loopsarise as individuals respond to deployed classifiers, and why relying onnon-causal features leads to arbitrarily bad post-adaptation risk. Instrategic classification, this framing highlights when adaptation resultsin genuine improvements versus gaming and how these responses reshapepost-adaptation distributions and long-term performance.

In addition, institutional policies determine which outcomes becomeobservable, introducing selection bias that complicates identification,evaluation, and the design of fair decision rules. By modeling selection aspart of the underlying causal process, it becomes possible to analyze howhistorical decisions limit what can be inferred from data and howalternative policies induce different observable populations. Together,these perspectives form a foundation for understanding the long-termdynamics of classifiers and populations, and for developing decision rulesthat remain robust to adaptation and policy-dependent shifts whilepromoting fairness and social welfare.