1.
Anderson, P. E. & Smith, J. Q. 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)., (2005).
2.
Bonet, B. A Calculus for Causal Relevance. in Proceedings of the Seventeen Conference on Uncertainty in Artificial Intelligence 40–47 (Morgan Kaufmann, 2001).
3.
Bonet, B. Instrumentality Tests Revisited. in Proceedings of the Seventeen Conference on Uncertainty in Artificial Intelligence 48–54 (Morgan Kaufmann Publishers, 2001).
4.
Capani, A., Niesi, G. & Robbiano, L. CoCoA 4. a system for doing Computations in Commutative Algebra. (2000).
5.
Char, B. W. Maple V library reference manual. (Springer-Verlag, 1991).
6.
Gale, W. A., AT & T Bell Laboratories, & Workshop on Artificial Intelligence and Statistics. Artificial intelligence and statistics. (Addison-Wesley Pub. Co, 1986).
7.
Dawid, A. P. Causal Inference Without Counterfactuals. Journal of the American Statistical Association 95, 407–424 (2000).
8.
Dawid, A. P. Influence Diagrams for Causal Modelling and Inference. International Statistical Review / Revue Internationale de Statistique 70, 161–189 (2002).
9.
Computation, causation, and discovery. (The MIT Press, 1999).
10.
Barndorff-Nielsen, O. E., Cox, D. R. & Klüppelberg, C. Complex stochastic systems. vol. Monographs on statistics and applied probability (Chapman & Hall/CRC).
11.
Cox, D. R., Klüppelberg, C. & Barndorff-Nielsen, O. E. Complex stochastic systems. vol. Monographs on statistics and applied probability (Chapman & Hall/CRC, 2001).
12.
Monroy, R. & Mexican International Conference on Artificial Intelligence. 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).
13.
Monroy, R. & Mexican International Conference on Artificial Intelligence. 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).
14.
Mond, D., Riccomagno, E. & Smith, J. Q. Algebraic causality : Bayes nets and beyond. Centre for Research in Statistical Methodology. Working papers, Vol.2007 (No.13)., (2007).
15.
Pearl, J. Comment: Graphical Models, Causality and Intervention. Statistical Science 8, 266–269 (1993).
16.
Pearl, J. Causal Diagrams for Empirical Research. Biometrika 82, 669–688 (1995).
17.
Pearl, J. Causality: models, reasoning, and inference. (Cambridge University Press, 2000).
18.
Proceedings of the 10th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. (Università La Sapienza, 2004).
19.
Riccomagno, E. & Smith, J. Q. The causal manipulation and Bayesian estimation of chain event graphs. (2005).
20.
Pronzato, L. & Zhigli︠a︡vskiĭ, A. A. Optimal design and related areas in optimization and statistics. vol. Springer optimization and its applications (Springer, 2008).
21.
Pearl, J. Statistics and causal inference: A review. Test 12, 281–345 (2003).
22.
Robins, J. 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, 1393–1512 (1986).
23.
Berkane, M. Latent variable modeling and applications to causality. vol. 120 (Springer).
24.
Scheines, R., Spirtes, P., Glymour, C., Meek, C. & Richardson, T. TETRAD 3: Tools for Causal Modeling. User’s Manual. http://www.phil.cmu.edu/tetrad/.
25.
Shafer, G. The art of causal conjecture. vol. Artificial intelligence (MIT Press, 1996).
26.
Spirtes, P., Glymour, C. N. & Scheines, R. Causation, prediction, and search. vol. Adaptive computation and machine learning (MIT Press, 2000).
27.
Spirtes, P., Glymour, C. N. & Scheines, R. Causation, prediction, and search. vol. Adaptive computation and machine learning (The MIT Press, 2000).
28.
Studený, M. Probabilistic conditional independence structures. vol. Information science and statistics (Springer, 2005).
29.
Studený, M. Probabilistic conditional independence structures. vol. Information science and statistics (Springer, 2005).
30.
Smith, J. Q. & Anderson, P. E. Conditional independence and chain event graphs. Artificial Intelligence 172, 42–68 (2008).
31.
Smith, J. Q. Bayesian Decision Analysis: Principles and Practice. (Cambridge University Press, 2010).
32.
Smith, J. Q. Bayesian decision analysis: principles and practice. (Cambridge University Press, 2010).
33.
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. (EDK, 2006).
34.
Thwaites, P. A. & Smith, J. Q. Evaluating Causal effects using Chain Event Graphs. in The third Workshop on Probabilistic Graphical Models 291–300.