ZHANG Jiji

Associate Professor

Academic Qualifications

  • Ph.D. in Logic, Computation and Methodology, Department of Philosophy, Carnegie Mellon University (2006)
  • M.S. in Logic and Computation, Department of Philosophy, Carnegie Mellon University (2002)
  • B.A. in Logic and Philosophy, Department of Philosophy, Peking University, China (2000)

Areas of Interest

Causal Modeling, Philosophy of Science, Epistemology, Artificial Intelligence

Work Experience

  • Associate Professor of Philosophy, Department of Philosophy, Lingnan University (Aug 2011 – present)
  • Assistant Professor of Philosophy, Department of Philosophy, Lingnan University (Aug 2008 – Aug 2011)
  • Assistant Professor of Philosophy, Division of the Humanities and Social Sciences, California Institute of Technology (Aug 2006 – Jul 2008)

Publications

  • J. Zhang, H, Liu, and T. Seidenfeld. (2018). “Agreeing to disagree and dilation”, International Journal of Approximate Reasoning.
  • A. Jaber, J. Zhang, and E. Bareinboim. (2018). “Causal Identification under Markov Equivalence”, Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI). (Best student paper award.)
  • A. Jaber, J. Zhang, and E. Bareinboim. (2018). “A graphical criterion for effect identification in equivalence classes of causal diagrams”, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI).
  • J. Zhang. (2017). “On the minimization principle in the Boolean approach to causal discovery”, in C. Yang, K. Lee, and H. Ono (eds.) Philosophical Logic: Current Trends in Asia. 
  • B. Huang, K. Zhang, J. Zhang, B. Sanchez-Romero, C. Glymour, and B. Scholkopf. (2017). “Behind distribution shift: mining driving forces of changes and causal arrows”, Proceedings of IEEE International Conference on Data Mining (ICDM).
  • K. Zhang, B. Huang, J. Zhang, B. Scholkopf, and C. Glymour. (2017). “Causal discovery from nonstationary/heterogeneous data: skeleton estimation and orientation determination”, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI).
  • Zhalama, J. Zhang, F. Eberhardt, and W. Mayer. (2017). “SAT-based causal discovery under weaker assumptions”, Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI).
  • K. Zhang, J. Zhang, B. Huang, B. Scholkopf, and C. Glymour. (2016) “On the Identifiability and estimation of functional causal models in the presence of outcome-dependent selection”, Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI).
  • Zhalama, J. Zhang, and W. Mayer. (2016). “Weakening faithfulness: some heuristic causal discovery algorithms”, International Journal of Data Science and Analytics.
  • J. Zhang and P. Spirtes. (2016). “The three faces of faithfulness”, Synthese.
  • J. Zhang and K. Zhang. (2015). “Likelihood and consilience: on Forster’s counterexamples to the likelihood theory of evidence”, Philosophy of Science.
  • K. Zhang, J. Zhang, and B. Scholkopf (2015). “Distinguishing cause from effect based on exogeneity”, Proceedings of the 15th Conference on Theoretical Aspects of Rationality and Knowledge (TARK).
  • J. Zhang and P. Spirtes. (2014). “Choice of units and the causal Markov condition”, in G. Guo and C. Liu (eds.) Scientific Explanation and Methodology of Science.
  • P. Spirtes and J. Zhang. (2014). “A uniformly consistent estimator of causal effects under the k-triangle-faithfulness assumption”, Statistical Science.
  • K. Zhang, Z. Wang, J. Zhang, and B. Schölkopf. (2014). “On estimation of functional causal models: general results and application to post-nonlinear causal model”, ACM Transactions on Intelligent Systems and Technology.
  • J. Zhang. (2013). “Can the incompatibilist get past the no past objection?”, dialectica.
  • R. De Clercq, W. Lam, and J. Zhang (2013). “Is there a problem with the causal criterion of event identity?”, American Philosophical Quarterly.
  • J. Zhang, W. Lam, and R. De Clercq. (2013). “A Peculiarity in Pearl’s Logic of Interventionist Counterfactuals”, Journal of Philosophical Logic.
  • J. Zhang. (2012). “A comparison of three Occam’s razors for Markovian causal models”, British Journal for the Philosophy of Science.
  • C. Bicchieri and J. Zhang. (2012). “An embarrassment of riches: modeling social preferences in ultimatum games”, in U. maki (ed.) Elsevier Handbook of the Philosophy of Economics.
  • J. Zhang. (2011). “A Lewisian logic of causal counterfactuals”, Minds and Machines.
  • J. Zhang and P. Spirtes. (2010). “Intervention, determinism, and the causal minimality condition”, Synthese.
  • J. Zhang. (2009). “Underdetermination in causal inference”, Studies in Logic.
  • C. Glymour, D. Danks, B. Glymour, F. Eberhardt, J. Ramsey, R. Scheines, P. Spirtes, C. Teng, and J. Zhang. (2009). “Actual cusation: a stone soup essay”, Synthese.
  • J. Zhang. (2008). “On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias”, Artificial Intelligence.
  • J. Zhang. (2008). “Causal reasoning with ancestral graphical models”, Journal of Machine Learning Research.
  • J. Zhang and P. Spirtes (2008). “Detection of unfaithfulness and robust causal inference”, Minds and Machines.
  • J. Zhang. (2008). “Error probabilities for inference of causal directions”, Synthese.
  • J. Zhang. (2007). “A characterization of Markov equivalence classes for causal models with latent variables”, Proceedings of Uncertainty in Artificial Intelligence (UAI). (Runner-up for the best paper award.)
  • J. Zhang (2007). “Generalized do-calculus with testable causal assumptions”, Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS).
  • J. Ramsey, P. Spirtes, and J. Zhang. (2006). “Adjacency faithfulness and conservative causal inference”, Proceedings of Uncertainty in Artificial Intelligence (UAI).
  • J. Zhang and P. Spirtes (2005). “A transformational characterization of Markov equivalence between causal models with latent variables”, Proceedings of Uncertainty in Artificial Intelligence (UAI).
  • R. Silva, J. Zhang, and J.G. Shanahan. (2005). “Probabilistic workflow mining”, Proceedings of Knowledge Discovery and Data Mining (KDD).
  • A. Ali, T. Richardson, P. Spirtes, and J. Zhang. (2005). “A step towards characterizing Markov equivalence classes of latent variable causal models”. Proceedings of Uncertainty in Artificial Intelligence (UAI).
  • J. Zhang and P. Spirtes. (2003). “Strong faithfulness and uniform consistency in causal inference”. Proceedings of Uncertainty in Artificial Intelligence (UAI).