Talk JdS 2025
Published in Journées de la Statistique, 2025
I had the pleasure of presenting at the JdS 2025 conference in Marseille. During my talk, I shared some ongoing work on policy learning aimed at maximizing a primary outcome while controlling for adverse events. I began by introducing both the causal inference framework and the classical policy learning approach. I then motivated our choice of the EP-learning method, to which we added a constraint specifically designed to handle adverse outcomes. Finally, I presented some preliminary results based on the uncorrected version of our method, which helped highlight the need for incorporating estimation corrections in future work.