Network Causation

{
  "id": "MLC-T-ENV-018465",
  "term": "Network Causation",
  "field": "Environment",
  "definition": "Network models represent causation graphically: nodes represent entities or states connected by arrows that represent models of individual causal processes or probabilities of the implied processes. The advantages of network models are that, unlike equations, they convey directionality and make explicit the structure of interactions in multivariate causal relationships. Empirical methods for analyzing causal networks include path analysis, structural equation models, and Bayesian network analysis. Alternatively, a network can be modeled mechanistically through mathematical simulation (e.g., systems of differential equations), but that is the old-fashioned field of systems analysis. Causal diagram theory, based on directed acyclic graphs, can be used to analyze complex causal relationships without parametric assumptions such as linearity that are required by structural equation modeling (Pearl 2000, Spirtes et al. 2000).",
  "subject": "Information management and systems",
  "references": [
    "CADDIS: Causal Concepts; Causal Concepts"
  ],
  "url": "https://mlchart.com/terminology/environment/network-causation/"
}