Causal criteria: time has come for a revision

Olsen, Jørn; Jensen, Uffe Juul

doi:10.1007/s10654-018-00479-x

Cited by 21 publications

(18 citation statements)

References 27 publications

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“…Although DAGs cannot represent the size of an association, they facilitate "bias analysis" (see Fig. 1) [14]. Investigators may use DAGs to highlight important variables that they were unable to condition on and consider their implications for the effect estimate, including residual confounding (from inaccurately or poorly measured variables, including confounders) [41].…”

Section: Strength Of Associationmentioning

confidence: 99%

“…Finally, the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology provides a systematic approach to assessing the certainty of a causal relationship based on a body of evidence (i.e., the existing studies available used to assess whether a causal relationship between an exposure and outcome exists). Epidemiologists have proposed that causal assessment may be improved by combining approaches such as these [7,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

et al. 2020

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The nine Bradford Hill (BH) viewpoints (sometimes referred to as criteria) are commonly used to assess causality within epidemiology. However, causal thinking has since developed, with three of the most prominent approaches implicitly or explicitly building on the potential outcomes framework: directed acyclic graphs (DAGs), sufficient-component cause models (SCC models, also referred to as ‘causal pies’) and the grading of recommendations, assessment, development and evaluation (GRADE) methodology. This paper explores how these approaches relate to BH’s viewpoints and considers implications for improving causal assessment. We mapped the three approaches above against each BH viewpoint. We found overlap across the approaches and BH viewpoints, underscoring BH viewpoints’ enduring importance. Mapping the approaches helped elucidate the theoretical underpinning of each viewpoint and articulate the conditions when the viewpoint would be relevant. Our comparisons identified commonality on four viewpoints: strength of association (including analysis of plausible confounding); temporality; plausibility (encoded by DAGs or SCC models to articulate mediation and interaction, respectively); and experiments (including implications of study design on exchangeability). Consistency may be more usefully operationalised by considering an effect size’s transportability to a different population or unexplained inconsistency in effect sizes (statistical heterogeneity). Because specificity rarely occurs, falsification exposures or outcomes (i.e., negative controls) may be more useful. The presence of a dose-response relationship may be less than widely perceived as it can easily arise from confounding. We found limited utility for coherence and analogy. This study highlights a need for greater clarity on BH viewpoints to improve causal assessment.

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Section: Strength Of Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

et al. 2020

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“…Moreover, their interpretation has changed over time as a result of major advancements in several scientific disciplines, analytical tools and access to big data [40]. As a consequence, some authors have called for revisions to Hill's criteria [41,42]. Several criteria are subject to interpretation, for instance the "experiment" criterion, or are difficult to differentiate from each other, such as "plausibility" and "coherence" [43,44].…”

Section: Discussionmentioning

confidence: 99%

Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

Gvozdenovic

Malvisi

Cinconze

et al. 2021

BMC Med Res Methodol

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Background Randomized controlled trials are considered the gold standard to evaluate causal associations, whereas assessing causality in observational studies is challenging. Methods We applied Hill’s Criteria, counterfactual reasoning, and causal diagrams to evaluate a potentially causal relationship between an exposure and outcome in three published observational studies: a) one burden of disease cohort study to determine the association between type 2 diabetes and herpes zoster, b) one post-authorization safety cohort study to assess the effect of AS04-HPV-16/18 vaccine on the risk of autoimmune diseases, and c) one matched case-control study to evaluate the effectiveness of a rotavirus vaccine in preventing hospitalization for rotavirus gastroenteritis. Results Among the 9 Hill’s criteria, 8 (Strength, Consistency, Specificity, Temporality, Plausibility, Coherence, Analogy, Experiment) were considered as met for study c, 3 (Temporality, Plausibility, Coherence) for study a, and 2 (Temporary, Plausibility) for study b. For counterfactual reasoning criteria, exchangeability, the most critical assumption, could not be tested. Using these tools, we concluded that causality was very unlikely in study b, unlikely in study a, and very likely in study c. Directed acyclic graphs provided complementary visual structures that identified confounding bias and helped determine the most accurate design and analysis to assess causality. Conclusions Based on our assessment we found causal Hill’s criteria and counterfactual thinking valuable in determining some level of certainty about causality in observational studies. Application of causal inference frameworks should be considered in designing and interpreting observational studies.

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“…The claim that a causal relationship exists has consequences on a societal level (taking action or not taking action). Olsen has called for the formulation of a criterion to determine when action should be taken and when not (7).…”

Section: The Criterion Of Consequentialitymentioning

confidence: 99%

“…Several publications in epidemiology have dealt with the ways in which causality can be inferred in the absence of an experiment, starting with the classic work of Bradford Hill and the nine aspects of causality (viewpoints) that he proposed (Box 2) (5) and continuing up to the present (6,7).…”

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confidence: 99%

Methods for Evaluating Causality in Observational Studies

Gianicolo

Eichler

Muensterer

et al. 2020

Deutsches Ärzteblatt International

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Background: In clinical medical research, causality is demonstrated by randomized controlled trials (RCTs). Often, however, an RCT cannot be conducted for ethical reasons, and sometimes for practical reasons as well. In such cases, knowledge can be derived from an observational study instead. In this article, we present two methods that have not been widely used in medical research to date. Methods: The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search. Results: Two relatively new approaches-regression-discontinuity methods and interrupted time series-can be used to demonstrate a causal relationship under certain circumstances. The regression-discontinuity design is a quasi-experimental approach that can be applied if a continuous assignment variable is used with a threshold value. Patients are assigned to different treatment schemes on the basis of the threshold value. For assignment variables that are subject to random measurement error, it is assumed that, in a small interval around a threshold value, e.g., cholesterol values of 160 mg/dL, subjects are assigned essentially at random to one of two treatment groups. If patients with a value above the threshold are given a certain treatment, those with values below the threshold can serve as control group. Interrupted time series are a special type of regression-discontinuity design in which time is the assignment variable, and the threshold is a cutoff point. This is often an external event, such as the imposition of a smoking ban. A before-and-after comparison can be used to determine the effect of the intervention (e.g., the smoking ban) on health parameters such as the frequency of cardiovascular disease. Conclusion: The approaches described here can be used to derive causal inferences from observational studies. They should only be applied after the prerequisites for their use have been carefully checked.

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Causal criteria: time has come for a revision

Cited by 21 publications

References 27 publications

Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

Methods for Evaluating Causality in Observational Studies

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