Causats

Causats is a term used in theoretical approaches to causality and complex systems to denote discrete causal units that propagate state changes through networks. Unlike single-step causes, causats capture multi-step, context-dependent pathways that produce effects across time and space in a system. They are often used in computational models and simulation studies to represent interventions and their ripple effects.

Etymology: The term is a neologism derived from causa, the Latin for cause, and has gained use in the 21st century among researchers studying higher-order causation and network dynamics.

Formal conception: In modeling, a causat is defined by triggering conditions, a propagation rule that governs how the effect spreads through connections, and a resultant state change in one or more components. Causats can be probabilistic, contextual, or deterministic, and their strength and reach may vary with system state.

Relation to other concepts: Causats extend direct causal links and causal graphs by encoding interactions, feedback, and temporal sequencing. They are compatible with interventions and counterfactual analyses, and can be composed into higher-order structures to represent complex policy or design changes.

Applications: Used in agent-based models, epidemiology, network science, AI explanations, and policy analysis to simulate how targeted actions influence broader outcomes.

Criticism: Critics argue that the formalization of causats risks ambiguity and that empirical identification remains challenging. Proponents contend they provide a useful abstraction for reasoning about complex causation.

See also: Causality; Causal graph; Intervention; Counterfactual; Complex systems.