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Hagmayer, Y., Sloman, S.A., Lagnado, D.A. and Waldmann, M.R. (2007) Causal Reasoning through Intervention. In: Gopnik, A. and Schulz, L., Eds., Causal Learning: Psychology, Philosophy, and Computation, Oxford University Press, Oxford, 86-100.
https://doi.org/10.1093/acprof:oso/9780195176803.003.0007

has been cited by the following article:

• TITLE: Causal Analysis of User Search Query Intent

  AUTHORS: Gahangir Hossain, James Haarbauer, Jonathan Abdo, Brian King

  KEYWORDS: Causal Bayesian Networks (CBNs), Query Search, Intervention, Reasoning, Inference Mechanisms, Prediction Methods

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.14, November 28, 2016

  ABSTRACT: We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.