causaliteitsinperking

Causaliteitsinperking is a Dutch term for the methodological practice of constraining causal claims to align with plausible mechanisms, known temporal order, and other forms of prior knowledge. In philosophy of science, statistics, and the social sciences it denotes restricting the space of possible causal explanations to avoid overinterpretation of observational data.

Practically, causaliteitsinperking is implemented by applying criteria such as temporal precedence, the possibility of intervention, and a plausible mechanism; by formalizing assumptions with structural causal models and directed acyclic graphs; and by requiring identifiability or robustness of effects under the constrained model. This approach emphasizes that causal claims should be coherent with theory and prior evidence, not only with statistical associations.

It is used in fields such as epidemiology, policy evaluation, and machine learning to guide causal discovery and estimation when randomized experiments are unavailable, ensuring that inferred relationships remain plausible within substantive knowledge.

Critics warn that constraining causality can introduce bias if the criteria reflect subjective judgments or incorrect theory, potentially obscuring real effects. The approach relies on transparent justification of the constraints and on sensitivity analyses to assess how conclusions change under different reasonable assumptions.

See also: causal inference, do-calculus, structural causal models, confounding, identifiability.