Literature DB >> 31566208

Invited Commentary: Counterfactuals in Social Epidemiology-Thinking Outside of "the Box".

Tyler J VanderWeele.   

Abstract

There are tensions inherent between many of the social exposures examined within social epidemiology and the assumptions embedded in quantitative potential-outcomes-based causal inference framework. The potential-outcomes framework characteristically requires a well-defined hypothetical intervention. As noted by Galea and Hernán (Am J Epidemiol. 2020;189(3):167-170), for many social exposures, such well-defined hypothetical exposures do not exist or there is no consensus on what they might be. Nevertheless, the quantitative potential-outcomes framework can still be useful for the study of some of these social exposures by creative adaptations that 1) redefine the exposure, 2) separate the exposure from the hypothetical intervention, or 3) allow for a distribution of hypothetical interventions. These various approaches and adaptations are reviewed and discussed. However, even these approaches have their limits. For certain important historical and social determinants of health such as social movements or wars, the quantitative potential-outcomes framework with well-defined hypothetical interventions is the wrong tool. Other modes of inquiry are needed.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  causal inference; counterfactuals; intervention; potential outcomes; race; social epidemiology

Year:  2020        PMID: 31566208      PMCID: PMC7217276          DOI: 10.1093/aje/kwz198

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


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8.  On Well-defined Hypothetical Interventions in the Potential Outcomes Framework.

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9.  On causal inference in the presence of interference.

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  3 in total

1.  Galea and Hernán Respond to "Brings to the Table," "Differential Measurement Error," and "Causal Inference in Social Epidemiology".

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3.  Complex systems models for causal inference in social epidemiology.

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