Consistency: A Critical but Often Overlooked Requirement for Causal Inference in Psychological Research

Psychologists are often asked to inform concrete actions ("Should we ban social media?") but tend to focus on vague questions ("Does ‘time spent on social media’ predict well-being?"). There is a mismatch between the causal effects researchers care about and the associations and correlations they compute. In this article, we highlight the consistency assumption as a foundational requirement for causal inference that can bridge this mismatch and make statistical estimations causally interpretable. Consistency shows how causal effects are interpretable only when (a) the causal effect of interest—the causal estimand—is described precisely enough that it does not bundle different versions of the treatment that have different effects, and (b) the way that the treatment variable changes in the data matches this effect of interest. We clarify these requirements and illustrate how to state and assess them in settings common in psychological research, with a focus on indirect experimental manipulations and multidimensional constructs. Making this step explicit helps researchers articulate their estimands more transparently and is a prerequisite for other concerns such as confounding and model specification.