Kausaation

Kausaation is a theoretical framework for analyzing causal relations in complex systems, emphasizing bidirectional influence, feedback, and emergent dynamics rather than a simple linear chain of events. In Kausaation, a variable can be both a cause and an effect within the same causal network, and causal strength can depend on the system's state and history.

The term fuses the Latin causa with causation and uses a K to distinguish this approach from standard causality in some discussions. It gained usage in interdisciplinary writings seeking to capture cyclic and context-dependent causality across disciplines such as biology, sociology, and information science.

Core ideas include modeling causality as a network or graph with directed edges that may form cycles, incorporating time delays, and allowing feedback loops. Interventions are analyzed with do-like reasoning on networks, but identifiability requires assumptions about network structure and dynamics. Quantitative tools include structural equation models with cyclic graphs, dynamic Bayesian networks, and dynamical systems theory.

Applications span systems biology, ecological and climate dynamics, organization theory, and policy modeling, where feedback and context strongly shape outcomes. Critics argue that causal direction can be indeterminate in loops, that models may become overfit or computationally intractable, and that Kausaation risks conflating correlation and causation if not carefully constrained.

See also: causality, causal loop, network causality, structural equation modeling, dynamic systems.