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Keywords:
causal model; conditioning; intervention; extension
causal model; conditioning; intervention; extension
Summary:
The knowledge of causal relations provides a possibility to perform predictions and helps to decide about the most reasonable actions aiming at the desired objectives. Although the causal reasoning appears to be natural for the human thinking, most of the traditional statistical methods fail to address this issue. One of the well-known methodologies correctly representing the relations of cause and effect is Pearl's causality approach. The paper brings an alternative, purely algebraic methodology of causal compositional models. It presents the properties of operator of composition, on which a general methodology is based that makes it possible to evaluate the causal effects of some external action. The proposed methodology is applied to four illustrative examples. They illustrate that the effect of intervention can in some cases be evaluated even when the model contains latent (unobservable) variables.
References:
[1] Detwarasiti, A., Shachter, R. D.: Influence diagrams for team decision analysis. Decision Analysis 2 (2005), 4, 207-228. DOI 10.1287/deca.1050.0047
[2] Hagmayer, Y., Sloman, S., Lagnado, D., Waldmann, M. R.: Causal reasoning through intervention. In: Causal Learning: Psychology, Philosophy, and Computation (A. Gopnik and L. Schulz, eds.), Oxford University Press 2007, pp. 86-101. DOI 10.1093/acprof:oso/9780195176803.003.0007
[3] Jiroušek, R.: Foundations of compositional model theory. Int. J. Gen. Syst. 40 (2011), 6, 623-678. DOI 10.1080/03081079.2011.562627 | MR 2817988 | Zbl 1252.68285
[4] Jiroušek, R.: On causal compositional models: Simple examples. In: Proc. 15th Int. Conf. on Inf. Processing and Management of Uncertainty - Part I. Springer 2014, pp. 517-526. DOI 10.1007/978-3-319-08795-5_53
[5] Malvestuto, F. M.: Equivalence of compositional expressions and independence relations in compositional models. Kybernetika 50 (2014), 3, 322-362. DOI 10.14736/kyb-2014-3-0322 | MR 3245534
[6] Malvestuto, F. M.: Marginalization in models generated by compositional expressions. To appear in Kybernetika 51 (2015), 4. MR 3350569
[7] Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, NY 2009. DOI 10.1017/cbo9780511803161 | MR 2548166 | Zbl 1188.68291
[8] Ryall, M., Bramson, A.: Inference and Intervention: Causal Models for Business Analysis. Routledge, NY 2013. DOI 10.4324/9780203076835
[9] Shachter, R.: Evaluating influence diagrams. Oper. Res. 34 (1986), 6, 871-882. DOI 10.1287/opre.34.6.871 | MR 0886655
[10] Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Springer Lecture Notes in Statistics, New York 1993. DOI 10.1007/978-1-4612-2748-9 | MR 1227558 | Zbl 0981.62001
[11] Tucci, R. R.: Introduction to Judea Pearl's Do-Calculus. arXiv:1305.5506v1 [cs.AI] (2013).
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