|Abstract||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. The similarity extends to the treatment of actions or interventions in the two theories. But there is also a crucial difference. 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 be ultimately the defining characteristic of causal dependence.|
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
|Through your library||Only published papers are available at libraries|
Similar books and articles
Matt Williams & Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.
Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.
Clark Glymour & David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Jon Williamson (2004). Bayesian Nets and Causality: Philosophical and Computational Foundations. OUP Oxford.
Added to index2010-07-24
Total downloads16 ( #81,653 of 722,700 )
Recent downloads (6 months)1 ( #60,006 of 722,700 )
How can I increase my downloads?