Toward practical causal epidemiology

Population attributable fraction (PAF), probability of causation, burden of disease, and related quantities derived from relative risk ratios are widely used in applied epidemiology and health risk analysis to quantify the extent to which reducing or eliminating exposures would reduce disease risks....

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Autor principal:	Louis Anthony Cox, Jr
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Causality Causal artificial intelligence Population attributable fraction Probability of causation Risk analysis Statistical methods Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/bb2207714bbc48ba8dcec8671c215be9
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spelling	oai:doaj.org-article:bb2207714bbc48ba8dcec8671c215be92021-11-14T04:35:20ZToward practical causal epidemiology2590-113310.1016/j.gloepi.2021.100065https://doaj.org/article/bb2207714bbc48ba8dcec8671c215be92021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2590113321000195https://doaj.org/toc/2590-1133Population attributable fraction (PAF), probability of causation, burden of disease, and related quantities derived from relative risk ratios are widely used in applied epidemiology and health risk analysis to quantify the extent to which reducing or eliminating exposures would reduce disease risks. This causal interpretation conflates association with causation. It has sometimes led to demonstrably mistaken predictions and ineffective risk management recommendations. Causal artificial intelligence (CAI) methods developed at the intersection of many scientific disciplines over the past century instead use quantitative high-level descriptions of networks of causal mechanisms (typically represented by conditional probability tables or structural equations) to predict the effects caused by interventions. We summarize these developments and discuss how CAI methods can be applied to realistically imperfect data and knowledge – e.g., with unobserved (latent) variables, missing data, measurement errors, interindividual heterogeneity in exposure-response functions, and model uncertainty. We recommend that CAI methods can help to improve the conceptual foundations and practical value of epidemiological calculations by replacing association-based attributions of risk to exposures or other risk factors with causal predictions of the changes in health effects caused by interventions.Louis Anthony Cox, JrElsevierarticleCausalityCausal artificial intelligencePopulation attributable fractionProbability of causationRisk analysisStatistical methodsInfectious and parasitic diseasesRC109-216ENGlobal Epidemiology, Vol 3, Iss , Pp 100065- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Causality Causal artificial intelligence Population attributable fraction Probability of causation Risk analysis Statistical methods Infectious and parasitic diseases RC109-216
spellingShingle	Causality Causal artificial intelligence Population attributable fraction Probability of causation Risk analysis Statistical methods Infectious and parasitic diseases RC109-216 Louis Anthony Cox, Jr Toward practical causal epidemiology
description	Population attributable fraction (PAF), probability of causation, burden of disease, and related quantities derived from relative risk ratios are widely used in applied epidemiology and health risk analysis to quantify the extent to which reducing or eliminating exposures would reduce disease risks. This causal interpretation conflates association with causation. It has sometimes led to demonstrably mistaken predictions and ineffective risk management recommendations. Causal artificial intelligence (CAI) methods developed at the intersection of many scientific disciplines over the past century instead use quantitative high-level descriptions of networks of causal mechanisms (typically represented by conditional probability tables or structural equations) to predict the effects caused by interventions. We summarize these developments and discuss how CAI methods can be applied to realistically imperfect data and knowledge – e.g., with unobserved (latent) variables, missing data, measurement errors, interindividual heterogeneity in exposure-response functions, and model uncertainty. We recommend that CAI methods can help to improve the conceptual foundations and practical value of epidemiological calculations by replacing association-based attributions of risk to exposures or other risk factors with causal predictions of the changes in health effects caused by interventions.
format	article
author	Louis Anthony Cox, Jr
author_facet	Louis Anthony Cox, Jr
author_sort	Louis Anthony Cox, Jr
title	Toward practical causal epidemiology
title_short	Toward practical causal epidemiology
title_full	Toward practical causal epidemiology
title_fullStr	Toward practical causal epidemiology
title_full_unstemmed	Toward practical causal epidemiology
title_sort	toward practical causal epidemiology
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/bb2207714bbc48ba8dcec8671c215be9
work_keys_str_mv	AT louisanthonycoxjr towardpracticalcausalepidemiology
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Toward practical causal epidemiology

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