Anderson, P. E., & Smith, J. Q. (2005). A graphical framework for representing the semantics of asymmetric models. University of Warwick, Centre for Research in Statistical Methodology Working papers Vol.2005 (No.12). http://wrap.warwick.ac.uk/35587/
Barndorff-Nielsen, O. E., Cox, D. R., & Klüppelberg, C. (n.d.). Complex stochastic systems: Vol. Monographs on statistics and applied probability [Electronic resource]. Chapman & Hall/CRC. http://0-marc.crcnetbase.com.pugwash.lib.warwick.ac.uk/isbn/9781420035988
Berkane, M. (n.d.). Latent variable modeling and applications to causality (Vol. 120). Springer. http://encore.lib.warwick.ac.uk/iii/encore/record/C__Rb3211176
Bonet, B. (2001a). A Calculus for Causal Relevance. Proceedings of the Seventeen Conference on Uncertainty in Artificial Intelligence, 40–47. http://arxiv.org/abs/1301.2257?
Bonet, B. (2001b). Instrumentality Tests Revisited. Proceedings of the Seventeen Conference on Uncertainty in Artificial Intelligence, 48–54. http://arxiv.org/abs/1301.2258?
Capani, A., Niesi, G., & Robbiano, L. (2000). CoCoA 4. a system for doing Computations in Commutative Algebra. http://cocoa.dima.unige.it
Char, B. W. (1991). Maple V library reference manual. Springer-Verlag.
Cooper, G. F., & Glymour, C. N. (Eds.). (1999). Computation, causation, and discovery. The MIT Press. http://0-cognet.mit.edu.pugwash.lib.warwick.ac.uk/book/computation-causation-and-discovery
Cox, D. R., Klüppelberg, C., & Barndorff-Nielsen, O. E. (2001). Complex stochastic systems: Vol. Monographs on statistics and applied probability. Chapman & Hall/CRC.
Dawid, A. P. (2000). Causal Inference Without Counterfactuals. Journal of the American Statistical Association, 95(450), 407–424. http://0-www.jstor.org.pugwash.lib.warwick.ac.uk/stable/2669377
Dawid, A. P. (2002). Influence Diagrams for Causal Modelling and Inference. International Statistical Review / Revue Internationale de Statistique, 70(2), 161–189. http://0-www.jstor.org.pugwash.lib.warwick.ac.uk/stable/1403901
Gale, W. A., AT & T Bell Laboratories, & Workshop on Artificial Intelligence and Statistics. (1986). Artificial intelligence and statistics. Addison-Wesley Pub. Co.
Information processing and management of uncertainty knowledge-based systems : proceedings = Traitment d’information et gestion d’incertitudes dans les systemes a base de connaissances : actes : July 2-7, 2006. (2006). EDK.
Mond, D., Riccomagno, E., & Smith, J. Q. (2007). Algebraic causality : Bayes nets and beyond. Centre for Research in Statistical Methodology. Working papers, Vol.2007 (No.13). http://wrap.warwick.ac.uk/35544
Monroy, R. & Mexican International Conference on Artificial Intelligence. (n.d.-a). MICAI 2004: advances in artificial intelligence : Third Mexican International Conference on Artificial Intelligence, Mexico City, Mexico, April 26-30, 2004 : proceedings (Vol. 2972). Springer-Verlag. https://0-link-springer-com.pugwash.lib.warwick.ac.uk/10.1007/b96521
Monroy, R. & Mexican International Conference on Artificial Intelligence. (n.d.-b). MICAI 2004: advances in artificial intelligence : Third Mexican International Conference on Artificial Intelligence, Mexico City, Mexico, April 26-30, 2004 : proceedings (Vol. 2972). Springer-Verlag. https://0-link-springer-com.pugwash.lib.warwick.ac.uk/10.1007/b96521
Pearl, J. (1993). Comment: Graphical Models, Causality and Intervention. Statistical Science, 8(3), 266–269. http://0-www.jstor.org.pugwash.lib.warwick.ac.uk/stable/2245965
Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669–688. http://0-www.jstor.org.pugwash.lib.warwick.ac.uk/stable/2337329
Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press.
Pearl, J. (2003). Statistics and causal inference: A review. Test, 12(2), 281–345. https://doi.org/10.1007/BF02595718
Proceedings of the 10th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. (2004). Università La Sapienza.
Pronzato, L., & Zhigli︠a︡vskiĭ, A. A. (2008). Optimal design and related areas in optimization and statistics: Vol. Springer optimization and its applications. Springer. http://0-link.springer.com.pugwash.lib.warwick.ac.uk/chapter/10.1007%2F978-0-387-79936-0_6
Riccomagno, E., & Smith, J. Q. (2005). The causal manipulation and Bayesian estimation of chain event graphs. http://wrap.warwick.ac.uk/35590/
Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9–12), 1393–1512. https://doi.org/10.1016/0270-0255(86)90088-6
Scheines, R., Spirtes, P., Glymour, C., Meek, C., & Richardson, T. (n.d.). TETRAD 3: Tools for Causal Modeling. User’s Manual. http://www.phil.cmu.edu/tetrad/
Shafer, G. (1996). The art of causal conjecture: Vol. Artificial intelligence. MIT Press.
Smith, J. Q. (2010a). Bayesian Decision Analysis: Principles and Practice. Cambridge University Press. http://0-dx.doi.org.pugwash.lib.warwick.ac.uk/10.1017/CBO9780511779237
Smith, J. Q. (2010b). Bayesian decision analysis: principles and practice. Cambridge University Press.
Smith, J. Q., & Anderson, P. E. (2008). Conditional independence and chain event graphs. Artificial Intelligence, 172(1), 42–68. https://doi.org/10.1016/j.artint.2007.05.004
Spirtes, P., Glymour, C. N., & Scheines, R. (2000a). Causation, prediction, and search: Vol. Adaptive computation and machine learning (2nd ed). MIT Press.
Spirtes, P., Glymour, C. N., & Scheines, R. (2000b). Causation, prediction, and search: Vol. Adaptive computation and machine learning (2nd edition). The MIT Press. http://0-cognet.mit.edu.pugwash.lib.warwick.ac.uk/book/causation-prediction-and-search
Studený, M. (2005a). Probabilistic conditional independence structures: Vol. Information science and statistics [Electronic resource]. Springer. http://0-link.springer.com.pugwash.lib.warwick.ac.uk/10.1007/b138557
Studený, M. (2005b). Probabilistic conditional independence structures: Vol. Information science and statistics. Springer.
Thwaites, P. A., & Smith, J. Q. (n.d.). Evaluating Causal effects using Chain Event Graphs. The Third Workshop on Probabilistic Graphical Models, 291–300. http://www.utia.cas.cz/files/mtr/pgm06/18_paper.pdf