| Research Areas: | - Causation, Formal Epistemology, Philosophy of Science, Artificial Intelligence
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| Current Projects: | - “Parsimony in Causal Inference: Epistemic Justifications and Methodological Implications”, Funded by the Research Grants Council of Hong Kong.
- “Logical Investigations of Causal Models and Counterfactual Structures”, Funded by the Research Grants Council of Hong Kong.
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| Recent Publications: | - Lin, H., and Zhang, J. (2020). “On Learning Causal Structures from Non-experimental Data without Any Faithfulness Assumption”. Proceedings of Machine Learning Research 117: 554-582.
- Huang, B., Zhang, K., Zhang, J., et al. (2020). “Causal Discovery from Heterogeneous/Nonstationary Data”, Journal of Machine Learning Research, 21: 1-53.
- Zhang, J., Seidenfeld, T., and Liu, H. (2019). “Subjective Causal Networks and Indeterminate Suppositional Credences”. Synthese, doi: 10.1007/s11229-019-02512-2.
- Jaber, A., Zhang, J., and Bareinboim, E. (2019). "Identification of conditional causal effects under Markov equivalence", Proceedings of the 33rd Annual Conference on Neural Information Processing Systems.
- Zhalama, Zhang, J., Eberhardt, F., et al. (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.
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| Selected Outputs | - Jaber, A., Zhang, J., and Bareinboim, E. (2019). “Causal Identification under Markov Equivalence: Completeness Results”, Proceedings of the 36th International Conference on Machine Learning, PMLR 97: 2981-2989.
- Zhang, J., Liu, H., and Seidenfeld, T. (2018). “Agreeing to Disagree and Dilation”. International Journal of Approximate Reasoning, 150-162.
- 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, J., and Zhang, K. (2015). “Likelihood and Consilience”, Philosophy of Science, 82(5): 930-940.
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| Selected Conference Presentations: | - “Error Probabilities in Causal Discovery and Popper’s Two Criteria of Simplicity”, Workshop on Predictive Processing, Direction of Fit, and Causal Inference, Beijing, China, 2020.
- “INUS and Occam’s Razors”, Workshop on Metaphilosophy and Philosophical Methodologies, Beijing, China, 2019.
- “Causal Models with Metaphysical Dependencies”, National Philosophy of Science Meeting, Hangzhou, China, 2019.
- “Causal Minimality in the Boolean Approach to Causal Inference”, The 16th Congress on Logic, Methodology, and Philosophy of Science and Technology, Prague, Czech, 2019.
- “A Characterization of Lewisian Causal Models”, The 16th Asian Logic Conference, Astana, Kazakhstan, 2019.
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| Selected Research Grants: | - PI, “Parsimony in Causal Inference: Epistemic Justifications and Methodological Implications”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2021 – 31/21/2022.
- PI, “Logical Investigations of Causal Models and Counterfactual Structures”, General Research Fund, Research Grants Council of Hong Kong, 01/09/2018 – 31/12/2020.
- PI, “Causation, Decision, and Imprecise Probabilities”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2016 – 31/12/2017.
- PI, “Philosophical Implications of Recent Advances in Causal Modeling”, General Research Fund, Research Grants Council of Hong Kong, 01/08/2013 – 31/01/2016.
- PI, “A Constructive Examination of Standard Assumptions in Causal Discovery”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2011 – 31/12/2012.
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Full CV