C. Kang, J. Tian, Markov Properties for Linear Causal Models with Correlated Errors, 2007, submitted.

Z. Cai, M. Kuroki, J. Pearl, and J Tian, Bounds on Direct Effects in the Presence of Confounded Intermediate Variables, 2007. Accepted to Biometrics. (Supplementary_materials)

J. Tian, On the Identification of a Class of Linear Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 22-26, 2007. Vancouver, Canada. AAAI Press, pp. 1284-1289.

J. Tian, A Criterion for Parameter Identification in Structural Equation Models, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 19-22, 2007. Vancouver, Canada. AUAI Press, pp. 392-399.

C. Kang, J. Tian, Polynomial Constraints in Causal Bayesian Networks, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 19-22, 2007. Vancouver, Canada. AUAI Press, pp. 200-208.

J. Tian, C. Kang, and J. Pearl, A Characterization of Interventional Distributions in Semi-Markovian Causal Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 16-20, 2006. Boston, Massachusetts. AAAI Press, pp. 1239-1244.

C. Kang and J. Tian, Inequality Constraints in Causal Models with Hidden Variables, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 13-16, 2006. Cambridge, Massachusetts. AUAI Press, pp. 233-240.

C. Kang and J. Tian, A Hybrid Generative/Discriminative Bayesian Classifier. Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS), May 11-13, 2006. Melbourne Beach, Florida. AAAI Press, pp. 562-567.

J. Tian, Identifying Direct Causal Effects in Linear Models, in Proceedings  of the National Conference on Artificial Intelligence (AAAI), 2005.

C. Kang and J. Tian,  Local Markov Property for Models Satisfying Composition Axiom, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2005. 

J. Tian,  Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2005. 

J. Tian, Identifying linear causal effects, in Proceedings  of the National Conference on Artificial Intelligence (AAAI), 2004.

J. Tian,  Identifying Conditional Causal Effects, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2004. 

J. Tian and J. Pearl, On the identification of causal effects, Technical Report, 2003. Submitted to the Journal of Artificial Intelligence.

J. Tian and J. Pearl, On the Testable Implications of Causal Models with Hidden Variables,  in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2002.

J. Tian and J. Pearl, A general identification condition for causal effects, in Proceedings  of the National Conference on Artificial Intelligence (AAAI), 2002.

J. Tian and J. Pearl, A new characterization of the experimental implications of causal Bayesian networks in Proceedings  of the National Conference on Artificial Intelligence (AAAI), 2002.

J. Tian and J. Pearl, ``Causal Discovery from Changes: a Bayesian Approach'', UCLA Cognitive Systems Laboratory, Technical Report (R-285), February 2001.

J. Tian and J. Pearl, ``Causal Discovery from Changes'', in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2001.

J. Tian and J. Pearl, ``Probabilities of causation: Bounds and identification'', in Annals of Mathematics and Artificial Intelligence 28 (2000) 287-313.

J. Tian, ``A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks'', in Craig Boutilier and Moises Goldszmidt (Eds.), Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-2000), San Francisco, CA: Morgan Kaufmann, 580--588, 2000.

J. Tian and J. Pearl, ``Probabilities of causation: Bounds and identification'', in Craig Boutilier and Moises Goldszmidt (Eds.), Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-2000), San Francisco, CA: Morgan Kaufmann, 589--598, 2000.

J. Tian, A. Paz, and J. Pearl, ``Finding minimal d-separating sets'', UCLA Cognitive Systems Laboratory, Technical Report (R-254), 1998.