91Ë¿¹ÏÊÓƵ

JOINT CORE/EBOH EPI Seminar Series Winter 2026

The Seminars in Epidemiology organized by the Department of Epidemiology, Biostatistics and Occupational Health at the 91Ë¿¹ÏÊÓƵ School of Population and Global Health is a self-approved Group Learning Activity (Section 1) as defined by the maintenance of certification program of the Royal College of Physicians and Surgeons of Canada. Physicians requiring accreditation, please complete the Evaluation Form and send to admincoord.eboh [at] mcgill.ca.

Philippe Boileau, PhD

Assistant Professor of Biostatistics
Department of Epidemiology, Biostatistics and Occupation Health
91Ë¿¹ÏÊÓƵ

WHEN: Monday, JANUARY 19, 2026, from 3:30-4:30pm
WHERE: Hybrid | 2001 91Ë¿¹ÏÊÓƵ College, Rm 1140 &
Onsite at 5252 boul. de Maisonneuve - 3rd floor, 3B Kitchen |
Note: Philippe Boileau will be presenting in-person at CORE

Abstract

The conditional average treatment effect (CATE) is frequently estimated to refute the homogeneous treatment effect assumption. Under this assumption, all units making up the population under study experience identical benefit from a given treatment. Uncovering heterogeneous treatment effects through inference about the CATE, however, requires that covariates truly modifying the treatment effect be reliably collected at baseline. CATE-based techniques will necessarily fail to detect violations when effect modifiers are omitted from the data due to, for example, resource constraints. Severe measurement error has a similar impact. To address these limitations, we prove that the homogeneous treatment effect assumption can be gauged through inference about contrasts of the potential outcomes’ variances. We derive causal machine learning estimators of these contrasts and study their asymptotic properties. We establish that these estimators are doubly robust and asymptotically linear under mild conditions, permitting formal hypothesis testing about the homogeneous treatment effect assumption even when effect modifiers are missing or mismeasured. Numerical experiments demonstrate that these estimators’ asymptotic guarantees are approximately achieved in experimental and observational data alike. These inference procedures are then used to detect heterogeneous treatment effects in the re-analysis of randomized controlled trials investigating targeted temperature management in cardiac arrest patients.

Speaker Bio

Philippe Boileau is an Assistant Professor of Biostatistics at 91Ë¿¹ÏÊÓƵ with a joint appointment in the Department of Epidemiology, Biostatistics and Occupational Health and the Department of Medicine. He is also a Junior Scientist at the Research Institute of the 91Ë¿¹ÏÊÓƵ Health Centre, where he is the director of the Novel Trial Methods Hub. 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, machine learning, and semiparametric techniques to provide dependable statistical inference without relying on convenience assumptions. His most recent work has focused on developing and applying causal machine learning methods for heterogeneous treatment effect discovery in clinical trial data.

Learning Objectives

At the completion of this talk, attendees will be able to:

• Describe limitations of existing statistical methods for heterogeneous treatment effect detection;
• Interpret differential variance parameters;
• Translate treatment effect homogeneity test results to clinical contexts.