The question
What kind of relational, causal or mechanistic claim does the evidence actually support?
Definition
Association, prediction, causal effect and mechanism answer different questions. Evidence supporting one constrains what can be said, but it cannot simply be promoted into another kind of claim.
Four scientific claims can sound almost interchangeable while meaning very different things.
Suppose students who use a particular study practice tend to perform better on exams.
We might say that use of the practice is associated with performance, that it predicts later performance, that increasing it causes performance to improve, or that it improves performance through a particular mechanism.
Those are four different claims.
Association and prediction
An association means that variables are related in the observed data.
If students who use a study practice more often also tend to perform better, the two variables are associated.
That relationship may be scientifically important, but it does not reveal the causal structure behind it. The practice might influence performance. Higher-performing students might be more likely to use it. Other factors might contribute to both.
Correlation is one familiar way of describing association, but the broader question is simply whether variables vary together.
Prediction asks something else.
A variable or model has predictive value when information from it helps forecast an outcome.
If knowing how students study improves forecasts of later performance, the study behaviour is predictive.
A predictor can be useful without being a cause.
Prediction can exploit patterns that are stable and informative even when their causal meaning is uncertain.
So: association asks whether variables are related. Prediction asks whether information helps forecast an outcome.
Neither question, by itself, tells us what causes what.
Why association does not settle causation
Suppose stress and poor sleep are associated.
Several causal structures could produce that relationship.
Stress may contribute to poor sleep. Poor sleep may contribute to stress. Both may influence each other. Other causal differences may contribute to both.
This creates problems of directionality and confounding.
Directionality concerns which way a causal relation runs, if one exists.
Confounding occurs when other causal differences are mixed into a comparison so that the observed relationship does not cleanly represent the causal effect of interest.
If two groups differ not only in a candidate cause but also in other relevant ways, their outcome difference can be difficult to interpret causally.
The association alone does not settle which causal structure produced it.
Causation asks about alternative conditions
A causal claim asks whether an outcome would differ under relevant alternative conditions of a candidate cause.
The simplest version is: what difference would changing X make to Y?
That is not the same as asking whether X and Y tend to occur together.
Interventions can provide especially useful causal evidence because they change a candidate factor rather than merely observe its natural variation.
Suppose researchers alter use of a study practice under a design that supports the relevant causal comparison and later observe differences in performance.
That can support the conclusion that changing the practice made a difference to the outcome.
But intervention is not synonymous with proof. What it establishes depends on whether the design supports the causal inference being made.
Causation is therefore not a stronger correlation.
It is a different kind of claim.
Once causal relevance has been established, Causes, Conditions, Triggers and Constraints distinguishes the different roles a causally relevant factor may play within an explanation.
A causal effect is not a mechanism
Now suppose strong evidence shows that changing the study practice improves performance.
We have learned that the intervention makes a causal difference.
We may still not know how.
The effect might involve stronger retrieval, greater engagement with the material, altered attention, or several interacting processes.
Knowing that an intervention works is not the same as knowing how it works.
A causal effect concerns whether changing one factor changes another under the relevant conditions.
A mechanism concerns the process through which that effect is produced.
Evidence about effects and mechanisms can inform one another, but they are not interchangeable.
Mechanism asks how the effect is produced
A mechanism is an organised process or set of causal relations through which an effect or phenomenon is produced.
That process need not be a simple linear chain. It may involve feedback, interacting components or multiple levels of organisation.
Nor is mechanism synonymous with neural mechanism.
The relevant level depends on the phenomenon and the explanatory question. A mechanism may involve behavioural processes, cognitive operations, bodily changes, interpersonal feedback, ecological conditions, neural processes, or relations among several of these.
A related distinction concerns mediators.
A mediator can identify an intermediate variable in a causal pathway, but identifying a mediator is not automatically the same as identifying the mechanism. A mechanism makes a broader claim about how the effect is produced.
Prediction is not explanation
Prediction can be scientifically valuable without revealing causal structure.
A model may accurately forecast relapse, performance or behaviour using previous observations and contextual information.
That does not by itself tell us which predictors are causes, what would happen if we intervened on them, or through what process the outcome is produced.
Predictive success alone does not establish causal or mechanistic explanation.
This does not place prediction below explanation.
Forecasting may be exactly the scientific goal.
The distinction matters only when predictive success is interpreted as though it had already answered a causal question.
Different questions require different evidence
These four categories should not be treated as a prestige ladder running from correlation up through prediction and causation to mechanism.
They differ in inferential commitment, not universal scientific value.
If the question is whether variables are related, association is the relevant target.
If the question is forecasting, prediction is the target.
If the question concerns what difference changing X would make, causal evidence is required.
If the question concerns how an effect is produced, mechanistic evidence becomes relevant.
The quality of the evidence depends on whether it answers the question being asked.
A study does not become weak because it stops at association when association is the question. A predictive model is not deficient because it does not identify a mechanism. A causal estimate can be highly informative even when the mechanism remains uncertain.
Four epistemic achievements
What varies with what? — Association.
What helps forecast what? — Prediction.
What difference would changing X make to Y? — Causation.
How is that effect produced? — Mechanism.
Each category licenses a different kind of conclusion.
Association can inform prediction. Prediction can generate causal hypotheses. Causal evidence can constrain candidate mechanisms. Mechanistic evidence can deepen causal understanding.
But:
Evidence supporting one kind of claim cannot simply be promoted into another.
Do not make a stronger claim than the evidence supports
Scientific language often drifts.
A variable is described as associated with an outcome, then as predicting it, then as causing it, then as explaining it.
Sometimes the evidence supports every transition.
Sometimes the language has moved further than the research.
Correlation, prediction, causation and mechanism answer different scientific questions. Knowing which one has actually been established is part of knowing what the evidence allows us to say.