Mediator
A mediator is an intermediate variable through which part or all of an exposure’s causal effect on an outcome operates.
Causal Inference for Data Science
A mediating variable is a variable through which part or all of the causal influence of one variable on another operates.
- How it is used
- Guides causal-effect analysis by identifying pathways through which treatment influences an outcome.
- How deep it goes
- Explains DAG roles, adjustment consequences, and linear-regression estimates, but defers nonlinear effects and counterfactual definitions.
- Examples
- Uses kidney-stone size, pageview platforms, and clinical-trial side effects to contrast mediators with other causal roles.
- What you can do after
- Diagnose mediator roles in causal graphs and choose whether adjustment estimates a total or direct effect.
Read how Causal Inference for Data Science explains mediator →
Causal AI
A mediator is an intermediate variable through which part or all of the causal influence of an exposure on an outcome operates.
- How it is used
- Supports front-door identification when backdoor adjustment cannot recover an exposure’s causal effect.
- How deep it goes
- Defines causal-path mediation, derives front-door estimands, and states three identification assumptions; it stops short of full do-calculus derivation.
- Examples
- Uses won virtual items between side-quest engagement and purchases, plus lung damage between smoking and lung cancer.
- What you can do after
- Evaluate whether a front-door estimand can identify a treatment effect despite unobserved confounding.
Related concepts
- front-door estimandused by · A front-door estimand is a causal-effect quantity identified from observational data through a mediator that satisfies the front-door identification conditions.
- causal adjustmentcontrasts with · Causal adjustment estimates a causal effect by conditioning on variables that block noncausal pathways and averaging over their target-population distribution.
- linear regressionconfigures · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
- direct effectconfigures · A direct effect is the portion of a causal effect transmitted along a specified direct causal path from a cause to an outcome, excluding effects through mediators.
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