Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: Mechanisms and the Evidence Hierarchy, Topoi 33(2):339-360, 2014.  doi: 10.1007/s11245-013-9220-9

Brendan Clarke, Bert Leuridan & Jon Williamson: Modelling mechanisms with causal cycles, Synthese 191(8):1651-1681, 2014. . doi: 10.1007/s11229-013-0360-7.

Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: The evidence that evidence-based medicine omits, Preventative Medicine 57:745-747, 2013. doi: 10.1016/j.ypmed.2012.10.020

Phyllis McKay Illari and Jon Williamson: In defence of activities, Journal of General Philosophy of Science, 44(1):69-83, 2013. doi: 10.1007/s10838-013-9217-5.

Federica Russo & Jon Williamson: EnviroGenomarkers: the interplay between mechanisms and difference making in establishing causal claims, Medicine Studies: International Journal for the History, Philosophy and Ethics of Medicine & Allied Sciences, 3:249–262, 2012. .

Phyllis McKay Illari and Jon Williamson: What is a mechanism: thinking about mechanisms across the sciences, European Journal for Philosophy of Science 2:119-135, 2012;

Federica Russo and Jon Williamson: Epistemic causality and evidence-based medicine, History and Philosophy of the Life Sciences 33(4):563-582, 2011.

Lorenzo Casini, Phyllis McKay Illari, Federica Russo and Jon Williamson: Models for prediction, explanation and control: recursive Bayesian networks, Theoria 26(1):5-33, 2011.

Barbara Osimani, Federica Russo and Jon Williamson: Scientific evidence and the law: an objective Bayesian formalisation of the precautionary principle in pharmaceutical regulation, Journal of Philosophy, Science and Law 11, 2011;

George Darby and Jon Williamson: Imaging Technology and the Philosophy of Causality, Philosophy and Technology 24(2): 115-136, 2011.

Federica Russo and Jon Williamson: Generic versus single-case causality: the case of autopsy, European Journal for Philosophy of Science 1(1): 47-69, 2011.

Phyllis McKay Illari and Jon Williamson: Function and organization: comparing the mechanisms of protein synthesis and natural selection, Studies in History and Philosophy of Biological and Biomedical Sciences 41, pp. 279-291, 2010, doi 10.1016/j.shpsc.2010.07.001; ;

Phyllis McKay Illari and Jon Williamson: Mechanisms are real and local, in Phyllis McKay Illari, Federica Russo and Jon Williamson (eds): Causality in the Sciences, Oxford University Press, pp. 818-844, 2011;

Lorenzo Casini, Phyllis McKay Illari, Federica Russo and Jon Williamson: Recursive Bayesian networks for prediction, explanation and control in cancer science: a position paper, Proceedings of the First International Conference on Bioinformatics, Valencia, 20-23 January 2010;

Jon Williamson: The philosophy of science and its relation to machine learning, in Mohamed Medhat Gaber (ed.): Scientific Data Mining and Knowledge Discovery: Principles and Foundations, Springer, pp. 77-89, 2009.

Jan-Willem Romeijn and Jon Williamson: Intervention, under-determination, and theory generation, unpublished manuscript 

Federica Russo and Jon Williamson: Interpreting causality in the health sciences, International Studies in the Philosophy of Science 21(2): 157-170, 2007.

Federica Russo and Jon Williamson: Interpreting probability in causal models for cancer, in Federica Russo and Jon Williamson (eds): Causality and probability in the sciences, London: College Publications, 2007, pp. 217-241.

Sylvia Nagl, Matt Williams and Jon Williamson: Objective Bayesian nets for systems modelling and prognosis in breast cancer, in Dawn Holmes and L.C. Jain (eds): `Innovations in Bayesian Networks: Theory and Applications’, Springer, 2008, pp. 131-167.

Sylvia Nagl, Matt Williams, Nadjet El-Mehidi, Vivek Patkar and Jon Williamson: Objective Bayesian nets for integrating cancer knowledge: a systems biology approach, in Juho Rousu, Samuel Kaski and Esko Ukkonen (eds): Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (Tuusula, Finland, 17-18 June 2006), Helsinki University Printing House, 2006, pp. 44-49. Video.

Matt Williams and Jon Williamson: Combining argumentation and Bayesian nets for breast cancer prognosis, Journal of Logic, Language and Information 15: 155-178, 2006.

Jon Williamson: From Bayesianism to the Epistemic View of Mathematics: Remarks motivated by Richard Jeffrey’s ‘Subjective probability: the real thing’, Philosophia Mathematica 14(3), pp. 365-369, 2006;

Jon Williamson: A dynamic interaction between machine learning and the philosophy of science, Minds and Machines 14(4), 2004, pp. 539-549;

Jung-Wook Bang, Raphael Chaleil & Jon Williamson: Two-stage Bayesian networks for metabolic network prediction, in Peter Lucas (ed), Proceedings of the Workshop on Qualitative and Model-Based Reasoning in Biomedicine, 9th Conference on Artificial Intelligence in Medicine Europe, 18-22 October 2003, Cyprus, pp. 19-23;

Jon Williamson: A probabilistic approach to diagnosis, Proceedings of the Eleventh International Workshop on Principles of Diagnosis (DX-00), Morelia, Michoacen, Mexico, June 8-11 2000.

Jon Williamson: Approximating discrete probability distributions with Bayesian networks, in Proceedings of the International Conference on Artificial Intelligence in Science and Technology, Hobart Tasmania, 16-20 December 2000.