Causades

Causades are a theoretical concept used to describe discrete units of causal influence within complex systems. A causade is defined as a minimal, self-contained causal module that links an initiating condition to a specified outcome, typically through an identifiable mechanism. Unlike a generic event or correlation, a causade implies potential causal power and can be recombined with other causades to form larger causal architectures.

Key characteristics of causades include modularity, temporal boundedness, and context sensitivity. Each causade has a clear starting point, a mechanism or process that connects cause to effect, and a defined outcome. In practice, causades are identified in empirical modelling by tracing interventions to their effects and separating stable causal sequences from spurious associations. They are useful for simplifying the analysis of complex networks by decomposing them into reusable causal building blocks.

Applications of the concept appear in philosophy of science, systems theory, and computational modelling, as well as in narratology and social science. In modelling, causades help organize hypotheses about how changes propagate through climate systems, economies, or social networks. In storytelling, they can function as modular plot elements that drive outcomes without prescribing the entire narrative path.

Critics caution that the notion can overstate separability in real systems and risk fragmentation of causality across contexts and temporal scales. Proponents argue that causades offer a pragmatic way to structure understanding and simulation of intricate causal relationships. Related topics include causality, causal chain, event structure, and interventionist accounts of causation.