TY - JOUR
T1 - Causal reasoning in epidemiology
T2 - Philosophy and logic
AU - Maldonado, George
AU - Cox, Louis Anthony
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Bias
KW - Causal reasoning
KW - Causation
KW - Counterfactuals
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U2 - 10.1016/j.gloepi.2020.100020
DO - 10.1016/j.gloepi.2020.100020
M3 - Article
AN - SCOPUS:85103767348
SN - 2590-1133
VL - 2
JO - Global Epidemiology
JF - Global Epidemiology
M1 - 100020
ER -