Causal reasoning in epidemiology: Philosophy and logic

George Maldonado, Louis Anthony Cox

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 ask well-specified causal questions; and 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. They should examine how the estimator falls short of approximating the “perfect” study design, conduct and analysis; adjust the study results for these shortcomings; and, 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 should lead to a well-defined ideal study design for answering causal questions and a mathematical expression for a measure of causal effect. We argue that the perspective of causal reasoning is an indispensable aid in producing study results that are useful for answering causal questions. It is also an indispensable aid in developing and refining epidemiologic methods for answering causal questions, as well as in understanding the attributes required of a method that is truly causal.

Original languageEnglish (US)
Article number100020
JournalGlobal Epidemiology
Volume2
DOIs
StatePublished - Nov 2020

Bibliographical note

Funding Information:
George Maldonado is the Editor-in-Chief of Global Epidemiology. The authors are grateful to the American Petroleum Institute (API) for grant number 2016-110225 , which funded the writing of an early draft manuscript on this topic. The API was not involved in the writing of that draft manuscript or this manuscript, and they did not review either manuscript.

Funding Information:
Special thanks to Karen Goodman, Andrew Ward and Anne Jurek for helpful comments on several versions of this manuscript. Thanks also to Sander Greenland, Pamela Mink, Carl Phillips, Sharon Schwartz and the students of Epidemiology Methods III (SPH 766, School of Public Health, University of Alberta) for helpful comments. George Maldonado is the Editor-in-Chief of Global Epidemiology. The authors are grateful to the American Petroleum Institute (API) for grant number 2016-110225, which funded the writing of an early draft manuscript on this topic. The API was not involved in the writing of that draft manuscript or this manuscript, and they did not review either manuscript.

Publisher Copyright:
© 2020 The Authors

Keywords

  • Bias
  • Causal reasoning
  • Causation
  • Counterfactuals

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