ArticleJournal of causal inference2022
Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes.
Article in Journal of causal inference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Loneliness and desire for thinness explain sex differences in adolescent internalizing problems.Nature human behaviour · 2026Article
- Cue-Based Authenticity Appraisals in Limited-Verifiability Contexts: A Two-Study Test in Historic Automotive Events.Behavioral sciences (Basel, Switzerland) · 2026Article
- Article
- Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder.Statistical methods in medical research · 2026Article
- The mediating role of recreational physical activity and dietary behavior in the relationship between family affluence and mental well-being: an interventional effects approach.Journal of behavioral medicine · 2026Article
- Students' motives for restricting academic freedom: Viewpoint discrimination and prosocial concerns.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Association of childhood and adult socioeconomic status with adult social connection: a mediation analysis.American journal of epidemiology · 2025Article
- Natural Effects in the Presence of an Intermediate Confounder: Evaluation of Pragmatic Estimation Strategies With an Emphasis on the Relationship Between Natural and Interventional Effects.Statistics in medicine · 2025Article
- Associations Between Social Determinants of Health, Allostatic Load, and Anthracycline Cardiotoxicity in a Diverse Patient Population.Journal of the American Heart Association · 2025Article
- Practical challenges in mediation analysis: a guide for applied researchers.Health services & outcomes research methodology · 2025Article
- Leveraging Experience Sampling/Ecological Momentary Assessment for Sociological Investigations of Everyday Life.Annual review of sociology · 2024Article
- An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs.BMC medical research methodology · 2024Article
- Exploring the potential mediating role of systemic antibiotics in the association between early-life lower respiratory tract infections and asthma at age 5 in the CHILD study.Frontiers in allergy · 2024Article
- Mental distress, COVID19 vaccine distrust and vaccine hesitancy in South Africa: A causal mediation regression analysis.PloS one · 2023Article
- Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.Statistics surveys · 2023Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This article provides a systematic explanation of such assumptions. We define five potential outcome types whose means are involved in various effect definitions. We tackle their mean/distribution's identification, starting with the one that requires the weakest assumptions and gradually building up to the one that requires the strongest assumptions. This presentation shows clearly why an assumption is required for one estimand and not another, and provides a succinct table from which an applied researcher could pick out the assumptions required for identifying the causal effects they target. Using a running example, the article illustrates the assembling and consideration of identifying assumptions for a range of causal contrasts. For several that are commonly encountered in the literature, this exercise clarifies that identification requires weaker assumptions than those often stated in the literature. This attention to the details also draws attention to the differences in the positivity assumption for different estimands, with practical implications. Clarity on the identifying assumptions of these various estimands will help researchers conduct appropriate mediation analyses and interpret the results with appropriate caution given the plausibility of the assumptions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.