Edge replacement and nonindependence in causation


Human beings show a robust nonindependence effect in causal reasoning: they predict that collateral effects should be correlated even given a common cause. This presents a problem for existing models of causal reasoning, as most predict independence. To deal with this problem, we propose an edge replacement process that builds up apparently probabilistic causal relations using hidden deterministic causes. This model allows us to fit nonindependence effects, and shows promise for modeling other phenomena, such as how causal relations change over time.

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