Abstract:
The treatment of one individual often affects the outcomes of others. A canonical example occurs in infectious disease settings, where vaccinating one individual can reduce disease transmission and thereby influence the health outcomes of others. This type of interference implies that individuals cannot plausibly be treated as independent and identically distributed (iid).
Extensive methodological research has recently addressed interference problems and the resulting violation of conventional iid assumptions. However, despite growing interest in this topic, there remains controversy over whether and when existing methods capture causal effects of practical interest, particularly in clinical medicine and public health.
In this talk, I will present causal methodologies—motivated by infectious disease settings—for addressing interference. The central idea is to define estimands that are insensitive to the interference structure. This approach is not merely a workaround to avoid interference; rather, I will argue that these estimands have a clear interpretation and can guide decisions by doctors and patients. Specifically, these estimands can quantify vaccine waning and sieve effects, as illustrated through examples concerning COVID-19 and HIV.
Short biography:
Mats Stensrud, MD Dr philos, is an associate professor of biostatistics at the Institute of Mathematics at EPFL in Switzerland. His research focuses on methods for causal inference in medicine and epidemiology, usually in settings with exposures and outcomes that depend on time.