A causal relationship, also known as causation and causality, exists when an event directly produces another event. In statistics, we say that a change in one variable produces a change in another variable. Establishing causality requires showing that the relationship is not due to chance or confounding factors, often through well-designed experiments or statistical methods.
Key aspects of a causal relationship include:
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Direct impact: The cause directly influences the effect, without any intervening factors.
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Time order: The cause must happen before the effect.
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Non-spurious: The relationship cannot be explained by a third, confounding variable.
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Plausible mechanism: There should be a logical or theoretical explanation for how the cause leads to the effect.
For example, a randomized controlled trial showing that a new medication lowers blood pressure provides evidence of a causal relationship between the medication and blood pressure reduction, because the study can account for chance, demonstrate time order, control for confounding variables, and propose a biological mechanism. Given the strength of the experimental design, researchers can confidently conclude that the medication is causing blood pressure to reduce.
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