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Bayesian Nets Are All There Is to Causal Dependence

Part of the Boston Studies in the Philosophy of Science book series (BSPS, volume 256)

The paper displays the similarity between the theory of probabilistic causation developed by Glymour et al. since 1983 and mine developed since 1976: the core of both is that causal graphs are Bayesian nets (section 4.2). The similarity extends to the treatment of actions or interventions in the two theories (section 4.4). But there is also a crucial difference (section 4.3): Glymour et al. take causal dependencies as primitive and argue them to behave like Bayesian nets under wide circumstances. By contrast, I argue the behavior of Bayesian nets to ultimately be the defining characteristic of causal dependence.

Keywords

Action Variable Markov Condition Manipulate Variable Causal Dependence Deterministic Causation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media B.V 2009

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