Matrices of Implied Causation (MICs) are a tool for understanding a path model’s implications for the causal influence of one variable on another, and for comparing models on that basis. While causality cannot be determined simply by fitting a path model, researchers often use these models as a representation of an underlying causal process model. In these cases, MICs and MIC tables are useful for comparing models for face validity, and for the design of optimal experiments to differentiate the predictions they make.
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