Abstract
The use of Bayesian networks for modeling causal systems has achieved widespread recognition with Judea Pearl’s Causality (2000). There, Pearl developed a ”do-calculus” for reasoning about the effects of deterministic causal interventions on a system. Here we discuss some of the different kinds of intervention that arise when indeterminstic interventions are allowed, generalizing Pearl’s account. We also point out the danger of the naive use of Bayesian networks for causal reasoning, which can lead to the mis-estimation of causal effects. We illustrate these ideas with a graphical user interface we have developed for causal modeling.
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References
Anderson, K.M., Odell, P.M., Wilson, P.W.F., Kannel, W.B.: Cardiovascular disease risk profiles. American Heart Journal 121, 293–298 (1991)
Cartwright, N.: What is wrong with Bayes nets? The Monist 84, 242–264 (2001)
Chickering, D.: A transformational characterization of equivalent Bayesian network structures. In: Poole, D., Besnard, P., Hanks, S. (eds.) Proc of the 11th Conference on Uncertainty in AI, pp. 87–98. Morgan Kaufmann, San Francisco (1995)
D’Agostino, R.B., Russell, M.W., Huse, D.M.: Primary and subsequent coronary risk appraisal: new results from the framingham study. American Heart Journal 139, 272–281 (2000)
Druzdzel, M.J.: SMILE: Structural modeling, inference, and learning engine and GeNIe: A development environment for graphical decision-theoretic models. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI 1999), Orlando, FL, July 18-22, pp. 902–903 (1999)
Halpern, J.Y., Pearl, J.: Causes and explanations: A structural-model approach – Part I: Causes. In: Breese, J., Koller, D. (eds.) Uncertainty in AI, pp. 194–202 (2001)
Halpern, J.Y., Pearl, J.: Causes and explanations: A structural-model approach – Part II: Explanation. In: IJCAI 2001 (2001)
Norsys. Netica (2000), http://www.norsys.com
Pearl, J.: Statistics, causality, and graphs. In: Gammerman, A. (ed.) Causal Models and Intelligent Data Management, pp. 3–16. Springer, Berlin (1999)
Pearl, J.: Causality: models, reasoning and inference. Cambridge University Press, Cambridge (2000)
Salmon, W.: Probabilistic causality. Pacific Phil Qtly 61, 50–74 (1980)
Spirtes, P., Glymour, C., Scheines, R.: Causation, prediction and search. Springer, New York (1993)
Suppes, P.: A Probabilistic Theory of Causality, Amsterdam (1970)
Woodward, J.: Causation and manipulability. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Fall 2001)
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Korb, K.B., Hope, L.R., Nicholson, A.E., Axnick, K. (2004). Varieties of Causal Intervention. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_35
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DOI: https://doi.org/10.1007/978-3-540-28633-2_35
Publisher Name: Springer, Berlin, Heidelberg
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