Causis

Causis is a term used in philosophy of science and cognitive science to denote a structured framework for analyzing causal relations. The name is derived from the root caus-, related to “cause,” and is used to discuss both everyday causal reasoning and formal models of causation. In its broadest sense, causis encompasses a family of theories that aim to explain how events influence one another, how causes are identified from observed data, and how causal knowledge can be used to predict and intervene in systems.

Core concepts include causal graphs and structural models, which represent variables and their direct influence relationships; counterfactual reasoning, which asks what would have happened under different conditions; and differentiation between necessary, sufficient, and contributory causes. Proponents emphasize the separability of correlation and causation, and the role of interventions in testing causal claims.

In practice, causis is applied in philosophy, statistics, economics, AI, and epidemiology, often using formal tools from structural causal models, Bayesian networks, and potential outcomes frameworks. It supports explanatory, predictive, and normative tasks, such as explaining past events, forecasting outcomes under policy changes, or planning interventions.

Limitations include dependence on model assumptions, identifiability issues, and the potential for misinterpretation when data are noisy or incomplete. Critics warn that causal models may overstate certainty or oversimplify complex systems. The term remains informal and is not tied to a single canonical theory; in different disciplines, causis is used to discuss related but distinct approaches to causation. See also: causality, causal inference, structural causal model, counterfactual, interventionist theory.