Pan Zhao defended his Phd “Topics in Causal Inference and Policy Learning with Applications to Precision Medicine” on Wednesday September 4 2024.
Title
Topics in Causal Inference and Policy Learning with Applications to Precision Medicine
Supervisors
Julie Josse (Inria) and Antoine Chambaz (Université Paris Cité)
The jury will be composed of
Alex LUEDTKE, Associate Professor, University of Washington (Rapporteur)
Agathe GUILLOUX, Directeur de recherche, Inria (Rapporteure)
Stefan WAGER, Associate Professor, Stanford Graduate School of Business (Examinateur)
Stijn VANSTEELANDT, Professor, Universiteit Gent (Examinateur)
Fabrizia MEALLI, Professor, European University Institute (Examinatrice)
Julie JOSSE, Directeur de recherche, Inria (Directrice de thèse)
Antoine CHAMBAZ, Professor, Université Paris Cité (Co-directeur de thèse)
Shu Yang, Associate Professor, North Carolina State University (Invité)
Abstract
This thesis explores advanced methods in causal inference, focusing on policy learning, instrumental variables (IV), and difference-in-differences (DiD). First, it addresses challenges of restrictive assumptions in IV and DiD methods, proposing an instrumented DiD approach to relax them. The thesis also introduces direct policy search to learn optimal policies, which come with novel identification results, and various estimators, ensuring robust policy learning under unmeasured confounding. It also presents a positivity-free policy learning framework using dynamic policies, enhancing incremental intervention effects with efficient machine learning methods. Additionally, it proposes a transfer learning framework for individualized treatment regimes in heterogeneous populations with survival data, offering robust tools for practical applications.
Slides
Website
panzhaooo.github.io
