| A general identification condition for causal effects J Tian, J Pearl Aaai/iaai, 567-573, 2002 | 505 | 2002 |
| Probabilities of causation: Bounds and identification J Tian, J Pearl Annals of Mathematics and Artificial Intelligence 28 (1), 287-313, 2000 | 287 | 2000 |
| Graphical models for inference with missing data K Mohan, J Pearl, J Tian Advances in neural information processing systems 26, 2013 | 222 | 2013 |
| Recovering from selection bias in causal and statistical inference E Bareinboim, J Tian, J Pearl Probabilistic and causal inference: The works of Judea Pearl, 433-450, 2022 | 210 | 2022 |
| Causal discovery from changes J Tian, J Pearl arXiv preprint arXiv:1301.2312, 2013 | 206 | 2013 |
| On the testable implications of causal models with hidden variables J Tian, J Pearl arXiv preprint arXiv:1301.0608, 2012 | 183 | 2012 |
| Bounds on direct effects in the presence of confounded intermediate variables Z Cai, M Kuroki, J Pearl, J Tian Biometrics 64 (3), 695-701, 2008 | 132 | 2008 |
| Finding minimal d-separators J Tian, A Paz, J Pearl Computer Science Department, University of California, 1998 | 114 | 1998 |
| A branch-and-bound algorithm for MDL learning Bayesian networks J Tian arXiv preprint arXiv:1301.3897, 2013 | 89 | 2013 |
| Partial counterfactual identification from observational and experimental data J Zhang, J Tian, E Bareinboim International conference on machine learning, 26548-26558, 2022 | 82 | 2022 |
| Recovering causal effects from selection bias E Bareinboim, J Tian Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 79 | 2015 |
| Estimating identifiable causal effects through double machine learning Y Jung, J Tian, E Bareinboim Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 12113 …, 2021 | 57 | 2021 |
| Bayesian model averaging using the k-best Bayesian network structures J Tian, R He, L Ram Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2010 | 54 | 2010 |
| Identifying dynamic sequential plans J Tian Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2008 | 53* | 2008 |
| Learning causal effects via weighted empirical risk minimization Y Jung, J Tian, E Bareinboim Advances in neural information processing systems 33, 12697-12709, 2020 | 49 | 2020 |
| Generalized adjustment under confounding and selection biases J Correa, J Tian, E Bareinboim Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 46 | 2018 |
| Joint Discovery of Skill Prerequisite Graphs and Student Models. Y Chen, JP González-Brenes, J Tian International Educational Data Mining Society, 2016 | 45 | 2016 |
| Testable implications of linear structural equation models B Chen, J Tian, J Pearl Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 43 | 2014 |
| Data poisoning attacks and defenses to crowdsourcing systems M Fang, M Sun, Q Li, NZ Gong, J Tian, J Liu Proceedings of the web conference 2021, 969-980, 2021 | 42 | 2021 |
| Inequality constraints in causal models with hidden variables C Kang, J Tian Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2006 | 38* | 2006 |
Judea PearlProfessor of Computer Science, University of California, Los Angeles在 cs.ucla.edu 的電子郵件地址已通過驗證
