Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Cristian Dragos Manta, le 17 décembre à 10h (mode hybride).

Titre : Towards More Flexible Causal Inference Algorithms for Real-World Datasets

Date: mercredi 17 décembre à 10h

Location: Auditorium 1, MILA, 2e étage

Jury

Résumé

From using the law of gravity to predict the orbits of planets, to understanding how human cells react to new chemicals, or predicting impactsof new policies on the economy, or increasing the fairness of loan offers,the field of causality finds applications in a large diversity of settings by providing a rich mathematical framework for performing probabilistic inference over the effects of perturbations on a system. Unfortunately,despite the promises of this field, the applications of causal discovery algorithms to real-world problems remain challenging. Why is that so? Using scientific discovery as a motivating example, we propose a possible approach to reduce the gap with real-world settings for predicting the effects of unseen interventions. Our method is grounded in using meta-learning for amortized causal discovery over different environments,while using a more flexible architecture for end-to-end causal effects prediction. This approach characterizes our broader research vision which consists in relaxing as many structural assumptions as possible from causal discovery methods and leveraging simulated data to improve sample efficiency, while maintaining the core causal inductive biases. In doing so, our goal is to develop flexible methods that can be extended to challenging settings where standard causal discovery assumptions are violated and where data might be scarce.