This year, The HESI Environmental Epidemiology Committee launched a webinar series to illustrate the critical role epidemiology can play in the field of quantitative risk assessment. The next webinar in the series, "Causal Reasoning in Epidemiology: Philosophy and Logic", will be presented by Dr. George Maldonado of the University of Minnesota.
Watch the webinar recording here.
This commentary adds to a lively discussion of causal modeling, reasoning and inference in the recent epidemiologic literature. We focus on fundamental philosophical and logical principles of causal reasoning in epidemiology, raising important points not emphasized in the recent discussion. To inform public health decisions that require answers to causal questions, studies should be approached as exercises in causal reasoning. They should do the following: ask well-specified causal questions; use estimators that approximate, given practical constraints, a “perfect” study, based on a clear definition of causation and a clear (and preferably, explicit) understanding of the philosophical basis for that definition; examine how the estimator falls short of approximating the “perfect” study design, conduct and analysis; adjust the study results for these shortcomings; in the publication of study results, clearly state the assumptions that were made in the design, conduct and analysis of the study, and discuss their plausibility for the topic under study. We argue that the explicit philosophical foundation for causal reasoning need not be counterfactual reasoning (currently in vogue in epidemiology), but it should lead to a well-defined ideal study design for answering causal questions and a mathematical expression for a measure of causal effect.
George Maldonado, PhD
George Maldonado is an Associate Professor at the University of Minnesota School of Public Health and has a PhD in epidemiology from UCLA. His focus is epidemiologic methods. He spends his time thinking about how we can do a better job of getting answers to causal questions from imperfect, non-experimental studies—most human health studies being both imperfect and non-experimental. He is the founding Editor-in-Chief of Global Epidemiology.
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