Philippe Boileau

Dr. Boileau is broadly interested in the development of assumption-lean statistical methods and their application to quantitative problems in the health and life sciences. Assumption-lean procedures combine causal inference and machine learning techniques to avoid unjustified assumptions about data-generating processes, ensuring reliable statistical inference. He is also committed to the development open-source statistical software and, more broadly, to the adoption of reproducible research practices. His most recent work has focused on causal machine learning methods for treatment effect modifier discovery in gene expression data collected during oncology trials.