Four-Way Decomposition
Also known as: 4-Way Decomposition, VanderWeele Four-Way Decomposition, Mediation-Interaction Decomposition, Unification of Mediation and Interaction
The four-way decomposition, introduced by Tyler VanderWeele in 2014, unifies the two great themes of effect analysis — mediation and interaction — into a single, exhaustive partition of a total causal effect. Any total effect of an exposure on an outcome can be split into exactly four pieces: a controlled direct effect (neither mediation nor interaction), a reference interaction (interaction but no mediation), a mediated interaction (both mediation and interaction at once), and a pure indirect effect (mediation but no interaction). These four components are mutually exclusive and add up to the total effect, and they nest the familiar two-way and three-way decompositions as special cases. Formalized in counterfactual notation and developed at book length in VanderWeele's 2015 Explanation in Causal Inference, the method gives social epidemiologists a precise vocabulary for asking how much of an exposure's effect runs through a mediator, how much depends on the exposure and mediator acting together, and how much is direct.
Key highlights
- Unifies mediation and interaction in one exhaustive, additive decomposition rather than treating them as competing analyses.
- Yields four mutually exclusive components that sum exactly to the total effect, giving a complete mechanistic accounting.
- Nests the standard two-way (direct/indirect) and three-way decompositions as special cases, with a clear interpretation of each piece.
- Estimable from familiar regression models with an interaction term, and supported by standard mediation software.
Intuition
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How it works
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When to use it
Use the four-way decomposition when you have an exposure, a single mediator, and an outcome, and you want to understand the mechanism of an effect more finely than a simple direct-versus-indirect split allows — in particular when you suspect the exposure and mediator may interact. It is the right tool when distinguishing 'effect through the mediator' from 'effect that requires the exposure and mediator together' is scientifically or policy-relevant, for example to judge whether intervening on the mediator would help and for whom. It requires the standard mediation identification assumptions: no unmeasured confounding of the exposure-outcome, mediator-outcome, and exposure-mediator relationships, and no mediator-outcome confounder affected by exposure. Prefer simpler two-way natural-effect decomposition when interaction is implausible or you lack power to estimate it. Avoid it when there are multiple causally ordered mediators or strong exposure-induced mediator-outcome confounding, which call for interventional or path-specific extensions.
Strengths & limitations
- Unifies mediation and interaction in one exhaustive, additive decomposition rather than treating them as competing analyses.
- Yields four mutually exclusive components that sum exactly to the total effect, giving a complete mechanistic accounting.
- Nests the standard two-way (direct/indirect) and three-way decompositions as special cases, with a clear interpretation of each piece.
- Estimable from familiar regression models with an interaction term, and supported by standard mediation software.
- Relies on strong, partly untestable no-unmeasured-confounding assumptions for exposure-outcome, mediator-outcome, and exposure-mediator relations.
- Invalid when a mediator-outcome confounder is itself affected by the exposure, which requires interventional or path-specific methods.
- Designed for a single mediator; multiple causally ordered mediators need extensions and the simple four-way split no longer applies.
- Interpretation of the controlled direct effect requires a chosen reference level for the mediator, and results can depend on that choice.
Common pitfalls
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Applications
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Frequently asked
What are the four components and how do they relate to mediation and interaction?
The four are: the controlled direct effect (neither mediation nor interaction — the effect with the mediator fixed at a reference), the reference interaction (interaction but no mediation — the joint effect that operates even without the exposure changing the mediator), the mediated interaction (both mediation and interaction — requires the exposure to change the mediator and the two to interact), and the pure indirect effect (mediation but no interaction — the effect transmitted through the mediator absent any interaction). They are mutually exclusive and sum to the total effect, so together they account for every part of the exposure's impact through these two lenses.
How does the four-way decomposition relate to natural direct and indirect effects?
It refines them. The familiar two-way split into natural direct and natural indirect effects can be recovered by grouping the four components: the natural direct effect equals the controlled direct effect plus the reference interaction, and the natural indirect effect equals the pure indirect effect plus the mediated interaction. The four-way version goes further by revealing how much of each natural effect is due to interaction versus 'pure' mechanism. This is why VanderWeele describes it as a unification — the standard mediation quantities are sums of its building blocks.
What assumptions are needed to interpret the components causally?
Beyond consistency and positivity, the decomposition requires four no-unmeasured-confounding conditions: no unmeasured confounding of the exposure-outcome relationship, of the mediator-outcome relationship, of the exposure-mediator relationship, and — critically — no mediator-outcome confounder that is itself affected by the exposure. The last condition is the one most often violated in observational social-epidemiologic data; when it fails, the natural-effect components are not identified and interventional or path-specific decompositions are needed instead. Sensitivity analysis, such as E-values for the mediation assumptions, is strongly recommended.
Sources
- 1.VanderWeele, T. J. (2014). A unification of mediation and interaction: a four-way decomposition. Epidemiology, 25(5), 749-761.
- 2.VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press.ISBN 9780199325870
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Cite this page
ScholarGate. (2026, June 23). Four-Way Decomposition. ScholarGate. https://scholargate.app/social-epidemiology/four-way-decomposition