Janie Coulombe (Universit茅 de Montr茅al)
Title: Recent advances in causal inference under irregular and informative observation times for the outcome
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Abstract:
Electronic health records (EHR) data contain rich information about patients鈥� health condition, comorbidities, clinical outcomes, and drug prescriptions. They are often used to draw causal inferences and compare different treatments鈥� effectiveness. However, these data are not experimental. They present with special features that should be addressed or that may affect the inference. One of these features is the irregular observation of the longitudinal processes used in the inference. In longitudinal studies in which we seek the causal effect of a treatment on a repeated outcome, for instance, covariate-dependent observation of the outcome has been shown to bias standard causal estimators. In this presentation, I will review recent work and present some of the most interesting findings in this area of research. Themes will include identifiability, efficiency, and alternatives to weighting methods to address irregular observation times.
Speaker
Janie Coulombe is an Assistant Professor in the Department of Mathematics and Statistics at Universit茅 de Montr茅al. She completed a PhD in Biostatistics from 缅北强奸 in 2021 and won the Pierre Robillard Award from the Statistical Society of Canada for her thesis in 2022. Her methodological research focuses on the development of causal estimators with good statistical properties that address the special features of electronic health records data, such as irregular observation times and missing data.