I grew up in central China, in a small city of Hubei Province by the Yangtze river. I received my undergraduate training at Beijing University, majoring in logic and philosophy. After I got my BA, I went to Carnegie Mellon University to pursue a PhD, initially hoping to become a mathematical logician. However, a course titled Probability and Artificial Intelligence shifted my academic interest, from formal deductive systems to inductive logic, epistemology, and methodology. I ended up writing a dissertation on causal reasoning, which has multiple points of contact with not only philosophy but also other disciplines such as computer science, psychology, and statistics.

My main research interests remain interdisciplinary to this day. The key questions that motivate my primary research program concern the extent to which information about cause and effect can be reliably inferred from passive observations (as opposed to active experiments), and the extent to which such inferred causal information can effectively guide predictions and decision-making. The attempts to address these questions are usually logical, epistemological, or even algorithmic in nature, but they are also related to important issues in the metaphysics of causation. I am grateful to the Research Grants Council of Hong Kong for having funded several of my projects in this research program, ranging from epistemological examinations of causal inference to the bearings of causal reasoning on decision theory. The research findings from these projects were published both in premier journals of philosophy and in leading venues of artificial intelligence and machine learning.

I taught previously at California Institute of Technology, Lingnan University, and Hong Kong Baptist University. While teaching in the Department of Religion and Philosophy at the Hong Kong Baptist University, I was also affiliated with the Ethical and Theoretical AI Lab and the Centre for Applied Ethics, and developed an interest in some issues of AI ethics, especially those that may be illuminated by causal modelling, such as algorithmic fairness and explainability.

• Causal Reasoning (in connection to AI)
• Philosophy of Science
• Formal Epistemology
• Philosophical Logic

• De Clercq, R., Fang, J., and Zhang, J. (2025) “Actual Causation and Minimality”, British Journal for the Philosophy of Science.
• Fang, J., and Zhang, J. (2025) “On Stalnakerian and Lewisian Causal Models”, Synthese.
• Hao, G., Zhang, J., Huang, B., Wang, H., and Zhang, K. (2024) “Natural Counterfactuals with Necessary Backtracking”, Proceedings of the 38th Annual Conference on Neural Information Processing Systems(NeurIPS).
• Fang, W., and Zhang, J. (2024). “Proportionality, Determinate Intervention Effects, and High-Level Causation”, Erkenntnis, https://doi.org/10.1007/s10670-024-00859-8.
• Zhang, J., and Zhang, K. (2023). “A New Minimality Condition for Boolean Accounts of Causal Regularities”, Erkenntnis, https://doi.org/10.1007/s10670-023-00685-4.
• Tang, Z., Zhang, J., and Zhang, K. (2023). “What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective”, ACM Computing Surveys, https://doi.org/10.1145/3597199.
• Zhang, J. (2022). “On the Unity between Observational and Experimental Causal Discovery”, THEORIA: An International Journal for Theory, History and Foundations of Science, 37(1): 63-74.
• Jaber, A., Ribeiro, A., Zhang, J., and Bareinboim, E. (2022). “Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness”, Proceedings of the 36th Annual Conference on Neural Information Processing Systems(NeurIPS).
• Zhang, J., Seidenfeld, T., and Liu, H. (2021). “Subjective Causal Networks and Indeterminate Suppositional Credences”, Synthese, 198: 6571-6597.
• Yin, Y., and Zhang, J. (2021). “Markov Categories, Causal Theories, and the Do-Calculus”, Studies in Logic, 14(6): 1-24.
• Lin, H., and Zhang, J. (2020). “On Learning Causal Structures from Non-experimental Data without Any Faithfulness Assumption”, Proceedings of the 31st International Conference on Algorithmic Learning Theory (ALT), PMLR 117: 554-582.
• Huang, B., Zhang, K., Zhang, J., Ramsey, J., Sanchez-Romero, R., Glymour, C., and Schölkopf, B. (2020). “Causal Discovery from Heterogeneous/Nonstationary Data”, Journal of Machine Learning Research, 21: 1-53.
• Zhalama, Zhang, J., Eberhardt, F., Mayer, W., and Li, J. (2019). “ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions”, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 1488-1494.
• Jaber, A., Zhang, J., and Bareinboim, E. (2019). “Causal Identification under Markov Equivalence: Completeness Results”, Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97: 2981-2989.
• Zhang, J., Liu, H., and Seidenfeld, T. (2018). “Agreeing to Disagree and Dilation”, International Journal of Approximate Reasoning, 150-162.
• Jaber, A., Zhang, J., and Bareinboim, E. (2018). “Causal Identification under Markov Equivalence”, Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 978-987.
• Zhang, J. (2017). “On the Minimization Principle in the Boolean Approach to Causal Discovery”, in Chin-Mu Yang, Kok Yong Lee, and Hiroakira Ono (eds.), Philosophical Logic: Current Trends in Asia, Springer.
• Zhalama, Zhang, J., and Mayer, W. (2017). “Weakening Faithfulness: Some Heuristic Causal Discovery Algorithms”, International Journal of Data Science and Analytics, 3(2): 93-104.
• Zhang, J., and Spirtes, P. (2016). “The Three Faces of Faithfulness”, Synthese, 193(4): 1011-1027.
• Zhang, K., Zhang, J., Huang, B., Schölkopf, B., and Glymour, C. (2016). “On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection”, Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 825-834.
• Zhang, J., and Zhang, K. (2015). “Likelihood and Consilience: on Forster’s Counterexamples to the Likelihood Theory of Evidence”, Philosophy of Science, 82(5): 930-940.
• Zhang, K., Wang, Z., Zhang, J., and Schölkopf, B. (2015). “On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model”, ACM Transactions on Intelligent Systems and Technology, 7(2): 13:1-13:22.
• Spirtes, P., and Zhang, J. (2014). “A Uniformly Consistent Estimator of Causal Effect Under the k-Triangle-Faithfulness Assumption”, Statistical Science, 29(4): 662-678.
• De Clercq, R., Lam, W., and Zhang, J. (2014). “Is There a Problem with the Causal Criterion of Event Identity?”, American Philosophical Quarterly, 51(2): 109-119.
• Zhang, J. (2013). “A Comparison of Three Occam’s Razors for Markovian Causal Models”, British Journal for the Philosophy of Science, 64(2): 423-448.
• Zhang, J. (2013). “Can the Incompatibilist Get Past the No Past Objection?”, dialectica, 67(3): 345-352.
• Zhang, J., Lam, W., and De Clercq, R. (2012). “A Peculiarity in Pearl’s logic of Interventionist Counterfactuals”, Journal of Philosophical Logic, 42(5): 783-794.
• Bicchieri C., and Zhang, J. (2012). “An Embarrassment of Riches: Modeling Social Preferences in Ultimatum Games”, in Uskali Mäki (ed.), Handbook of the Philosophy of Science, Vol. 13, Philosophy of Economics, pp. 577-596, Amsterdam: Elsevier.
• Zhang, J. (2011). “A Lewisian Logic of Causal Counterfactuals”, Minds and Machines, 23(1): 77-93.
• Zhang, J., and Spirtes, P. (2011). “Intervention, Determinism, and the Causal Minimality Condition”, Synthese, 182(3): 335-347.