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Review
. 2025 Feb 16;54(2):dyaf015.
doi: 10.1093/ije/dyaf015.

Inconsistent consistency: evaluating the well-defined intervention assumption in applied epidemiological research

Affiliations
Review

Inconsistent consistency: evaluating the well-defined intervention assumption in applied epidemiological research

Jerzy Eisenberg-Guyot et al. Int J Epidemiol. .

Abstract

Background: According to textbook guidance, satisfying the well-defined intervention assumption is key for estimating causal effects. However, no studies have systematically evaluated how the assumption is addressed in research. Thus, we reviewed how researchers using g-methods or targeted maximum likelihood estimation (TMLE) interpreted and addressed the well-defined intervention assumption in epidemiological studies.

Methods: We reviewed observational epidemiological studies that used g-methods or TMLE, were published from 2000-21 in epidemiology journals with the six highest 2020 impact factors and met additional criteria. Among other factors, reviewers assessed if authors of included studies aimed to estimate the effects of hypothetical interventions. Then, among such studies, reviewers assessed whether authors discussed key causal-inference assumptions (e.g. consistency or treatment variation irrelevance), how they interpreted their findings and if they specified well-defined interventions.

Results: Just 20% (29/146) of studies aimed to estimate the effects of hypothetical interventions. Of such intervention-effect studies, almost none (1/29) stated 'how' the exposure would be intervened upon; among those that did not state a 'how', the 'how' mattered for consistency (i.e., for treatment variation irrelevance) in 64% of studies (18/28). Moreover, whereas 79% (23/29) of intervention-effect studies mentioned consistency, just 45% (13/29) interpreted findings as corresponding to the effects of hypothetical interventions. Finally, reviewers determined that just 38% (11/29) of intervention-effect studies had well-defined interventions.

Conclusions: We found substantial deviations between guidelines regarding meeting the well-defined intervention assumption and researchers' application of the guidelines, with authors of intervention-effect studies rarely critically examining the assumption's validity, let alone specifying well-defined interventions.

Keywords: Consistency; causal inference; potential outcomes; stable unit treatment value assumption; treatment variation irrelevance; well-defined intervention.

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Conflict of interest statement

None declared.

Figures

Figure 1
Figure 1
Article inclusion and exclusion flow diagram. aWe chose 2000 as the earliest publication year because Robins et al. published an article in that year on marginal structural modelling which introduced g-methods to much of the field; see reference 38 in the reference list for additional details. bAsterisk symbol (‘*’) in search term used to search for all terms that began with the given root.

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References

    1. Hernán MA. Does water kill? A call for less casual causal inferences. Ann Epidemiol 2016;26:674–80. - PMC - PubMed
    1. Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol 2016;45:1776–86. - PMC - PubMed
    1. Broadbent A. Causation and prediction in epidemiology: a guide to the “Methodological Revolution”. Stud Hist Philos Biol Biomed Sci 2015;54:72–80. - PubMed
    1. Winship C, Morgan SL. The estimation of causal effects from observational data. Annu Rev Sociol 1999;25:659–706.
    1. Basso O, Naimi AI. Commentary: from estimation to translation. Epidemiology 2015;26:27–9. - PubMed