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Causality and PsychopathologyFinding the Determinants of Disorders and their Cures$
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Patrick Shrout, Katherine Keyes, and Katherine Ornstein

Print publication date: 2011

Print ISBN-13: 9780199754649

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199754649.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 03 December 2021

What Would Have Been Is Not What Would Be: Counterfactuals of the Past and Potential Outcomes of the Future

What Would Have Been Is Not What Would Be: Counterfactuals of the Past and Potential Outcomes of the Future

Chapter:
2 (p.25) What Would Have Been Is Not What Would Be: Counterfactuals of the Past and Potential Outcomes of the Future
Source:
Causality and Psychopathology
Author(s):

Sharon Schwartz

Nicolle M. Gatto

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199754649.003.0006

Epidemiology is often described as the basic science of public health. A mainstay of epidemiologic research is to uncover the causes of disease that can serve as the basis for successful public-health interventions (e.g., Institute of Medicine, 1988; Milbank Memorial Fund Commission, 1976). A major obstacle to attaining this goal is that causes can never be seen but only inferred. For this reason, the inferences drawn from our studies must always be interpreted with caution. Considerable progress has been made in the methods required for sound causal inference. Much of this progress is rooted in a full and rich articulation of the logic behind randomized controlled trials (Holland, 1986). From this work, epidemiologists have a much better understanding of barriers to causal inference in observational studies, such as confounding and selection bias, and their tools and concepts are much more refined. The models behind this progress are often referred to as ‘‘counterfactual’’ models. Although researchers may be unfamiliar with them, they are widely (although not universally) accepted in the field. Counterfactual models underlie the methodologies that we all use. Within epidemiology, when people talk about a counterfactual model, they usually mean a potential outcomes model—also known as ‘‘Rubin’s causal model.’’ As laid out by epidemiologists, the potential outcomes model is rooted in the experimental ideas of Cox and Fisher, for which Neyman provided the first mathematical expression. It was popularized by Rubin, who extended it to observational studies, and expanded by Robins to exposures that vary over time (Maldonado & Greenland, 2002; Hernan, 2004; VanderWeele & Hernan, 2006). This rich tradition is responsible for much of the progress we have just noted. Despite this progress in methods of causal inference, a common charge in the epidemiologic literature is that public-health interventions based on the causes we identify in our studies often fail.

Keywords:   Causal explanation, Depression, Exchangeability, Mediation analysis, Natural confounding, Potential outcomes model, Schizophrenia

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