Causal inference and effect estimation using observational data

Igelström, E. , Craig, P. , Lewsey, J. , Lynch, J., Pearce, A. and Katikireddi, S. V. (2022) Causal inference and effect estimation using observational data. Journal of Epidemiology and Community Health, 76(11), pp. 960-966. (doi: 10.1136/jech-2022-219267) (PMCID:PMC9554068)

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Abstract

Observational studies aiming to estimate causal effects often rely on conceptual frameworks that are unfamiliar to many researchers and practitioners. We provide a clear, structured overview of key concepts and terms, intended as a starting point for readers unfamiliar with the causal inference literature. First, we introduce theoretical frameworks underlying causal effect estimation methods: the counterfactual theory of causation, the potential outcomes framework, structural equations and directed acyclic graphs. Second, we define the most common causal effect estimands, and the issues of effect measure modification, interaction and mediation (direct and indirect effects). Third, we define the assumptions required to estimate causal effects: exchangeability, positivity, consistency and non-interference. Fourth, we define and explain biases that arise when attempting to estimate causal effects, including confounding, collider bias, selection bias and measurement bias. Finally, we describe common methods and study designs for causal effect estimation, including covariate adjustment, G-methods and natural experiment methods.

Item Type:Articles
Additional Information:EI, PC, SVK and AP receive funding from the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). SVK is supported by an National Health Service Research Scotland Senior Clinical Fellowship (SCAF/15/02) and a European Research Council Starter grant (949582). AP is supported by the Wellcome Trust (205412/Z/16/Z).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Katikireddi, Professor Vittal and Craig, Professor Peter and Igelström, Erik and Lewsey, Professor Jim and Pearce, Dr Anna
Creator Roles:
Igelström, E.Conceptualization, Writing – original draft
Craig, P.Conceptualization, Writing – review and editing, Supervision
Lewsey, J.Conceptualization, Writing – review and editing, Supervision
Pearce, A.Conceptualization, Writing – review and editing
Katikireddi, V.Conceptualization, Writing – review and editing, Supervision
Authors: Igelström, E., Craig, P., Lewsey, J., Lynch, J., Pearce, A., and Katikireddi, S. V.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU
Journal Name:Journal of Epidemiology and Community Health
Publisher:BMJ Publishing Group
ISSN:0143-005X
ISSN (Online):1470-2738
Published Online:06 September 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of Epidemiology and Community Health 76(11): 960-966
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3048231Inequalities in healthAlastair LeylandMedical Research Council (MRC)MC_UU_00022/2HW - MRC/CSO Social and Public Health Sciences Unit
3048231Inequalities in healthAlastair LeylandOffice of the Chief Scientific Adviser (CSO)SPHSU17HW - MRC/CSO Social and Public Health Sciences Unit
172690Understanding the impacts of welfare policy on health: A novel data linkage studySrinivasa KatikireddiOffice of the Chief Scientific Adviser (CSO)SCAF/15/02HW - Public Health
174091Improving life chances & reducing child health inequalities: harnessing the untapped potential of existing dataAnna PearceWellcome Trust (WELLCOTR)205412/Z/16/ZHW - MRC/CSO Social and Public Health Sciences Unit