causals

Causals refers to aspects of cause-and-effect relationships and the reasoning about such relationships, across fields. In philosophy, statistics, epidemiology, economics, and computer science, a causal relation denotes that changing one thing (the cause) would change another (the effect) under some conditions. Causal reasoning aims to identify, explain, and intervene in these relationships, often using models to represent mechanisms and dependencies.

A central challenge is distinguishing causation from correlation. Two variables may move together without one causing the other due to confounding, common causes, or chance. Causal inference seeks to estimate the magnitude of a causal effect—the change in an outcome that would result from actively changing the cause—while controlling for alternative explanations.

Common methods include randomized controlled trials, which assign treatments at random; quasi-experimental designs such as instrumental variables, regression discontinuity, and difference-in-differences; and formal frameworks like the potential outcomes approach and do-calculus with graphical models. In time series, Granger causality tests whether past values of one variable help predict another.

In philosophy, accounts of causality include regularity theories, interventionist (manipulationist) theories, and probabilistic causal notions. Graphical causal models and DAGs are widely used to encode assumed causal structure and to guide analysis.

Limitations include unmeasured confounding, selection bias, measurement error, and model misspecification. The study of causals encompasses theory, methodology, and practical application in science and policy, with the goal of understanding how interventions could produce the desired changes in outcomes.