Séminaire départemental – Differentiable Causal Discovery with Observational and Interventional Data

Résumé: Knowledge of the causal structure that underlies a data generating process is essential to answering questions of causal nature. Such questions are abundant in fields that involve decision making such as econometrics, epidemiology, and social sciences. When causal knowledge is unavailable, one can resort to causal discovery algorithms, which attempt to recover causal relationships from data. This talk will present a new algorithm for this task, that combines continuous-constrained optimization with the flexible density estimation capabilities of normalizing flows. In contrast with previous work in this direction, our method combines observational and interventional data to improve identification of the causal graph. We will present empirical results, along with a theoretical justification of our algorithm.

Biographie: Alexandre Drouin is a Research Scientist at Element AI in Montréal, Canada and an Adjunct Professor of computer science at Laval University. He received a PhD in machine learning from Laval University in 2019 for his work on antibiotic resistance prediction in bacterial genomes. His interests include causal inference, deep learning, and bioinformatics.

Note: Cette présentation sera donnée en français