Jin Tian

Publication Activity (10 Years)

Years Active: 2000-2023
Publications (10 Years): 25

Top Topics

Bayesian Network Structures

Equivalence Class

Top Venues

Publications

Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Joint Treatment Effects by Combining Multiple Experiments. ICML (2023)
Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
Causal Effect Identification in Cluster DAGs. AAAI (2023)
Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. NeurIPS (2023)
Hebi Li, Forrest Sheng Bao, Qi Xiao, Jin Tian
Codepod: A Namespace-Aware, Hierarchical Jupyter for Interactive Development at Scale. CoRR (2023)
Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
On Measuring Causal Contributions via do-interventions. ICML (2022)
Hyunchai Jeong, Jin Tian, Elias Bareinboim
Finding and Listing Front-door Adjustment Sets. CoRR (2022)
Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
Effect Identification in Cluster Causal Diagrams. CoRR (2022)
Hyunchai Jeong, Jin Tian, Elias Bareinboim
Finding and Listing Front-door Adjustment Sets. NeurIPS (2022)
Junzhe Zhang, Jin Tian, Elias Bareinboim
Partial Counterfactual Identification from Observational and Experimental Data. ICML (2022)
Junzhe Zhang, Jin Tian, Elias Bareinboim
Partial Counterfactual Identification from Observational and Experimental Data. CoRR (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. NeurIPS (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. ICML (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Identifiable Causal Effects through Double Machine Learning. AAAI (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Learning Causal Effects via Weighted Empirical Risk Minimization. NeurIPS (2020)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Causal Effects Using Weighting-Based Estimators. AAAI (2020)
Mojdeh Saadati, Jin Tian
Adjustment Criteria for Recovering Causal Effects from Missing Data. ECML/PKDD (1) (2019)
Mojdeh Saadati, Jin Tian
Adjustment Criteria for Recovering Causal Effects from Missing Data. CoRR (2019)
Juan D. Correa, Jin Tian, Elias Bareinboim
Identification of Causal Effects in the Presence of Selection Bias. AAAI (2019)
Juan D. Correa, Jin Tian, Elias Bareinboim
Adjustment Criteria for Generalizing Experimental Findings. ICML (2019)
Juan D. Correa, Jin Tian, Elias Bareinboim
Generalized Adjustment Under Confounding and Selection Biases. AAAI (2018)
Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian
Learning Bayesian network structures under incremental construction curricula. Neurocomputing 258 (2017)
Jin Tian
Recovering Probability Distributions from Missing Data. ACML (2017)
Ru He, Jin Tian, Huaiqing Wu
Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling. J. Mach. Learn. Res. 17 (2016)
Jin Tian
Recoverability of Joint Distribution from Missing Data. CoRR (2016)
Yetian Chen, José P. González-Brenes, Jin Tian
Joint Discovery of Skill Prerequisite Graphs and Student Models. EDM (2016)
Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian
Curriculum Learning of Bayesian Network Structures. ACML (2015)
Ru He, Jin Tian, Huaiqing Wu
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling. CoRR (2015)
Yetian Chen, Lingjian Meng, Jin Tian
Exact Bayesian Learning of Ancestor Relations in Bayesian Networks. AISTATS (2015)
Jin Tian
Missing at Random in Graphical Models. AISTATS (2015)
Elias Bareinboim, Jin Tian
Recovering Causal Effects from Selection Bias. AAAI (2015)
Yetian Chen, Jin Tian, Olga Nikolova, Srinivas Aluru
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks. CoRR (2014)
Elias Bareinboim, Jin Tian, Judea Pearl
Recovering from Selection Bias in Causal and Statistical Inference. AAAI (2014)
Yetian Chen, Jin Tian
Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging. AAAI (2014)
Bryant Chen, Jin Tian, Judea Pearl
Testable Implications of Linear Structural Equation Models. AAAI (2014)
Jin Tian
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks CoRR (2013)
Jin Tian, Judea Pearl
Probabilities of Causation: Bounds and Identification CoRR (2013)
Jin Tian, Judea Pearl
On the Testable Implications of Causal Models with Hidden Variables CoRR (2013)
Jin Tian, Judea Pearl
Causal Discovery from Changes CoRR (2013)
Ru He, Jiong Wang, Jin Tian, Cheng-Tao Chu, Bradley Mauney, Igor Perisic
Session analysis of people search within a professional social network. J. Assoc. Inf. Sci. Technol. 64 (5) (2013)
Karthika Mohan, Judea Pearl, Jin Tian
Graphical Models for Inference with Missing Data. NIPS (2013)
Jin Tian
Identifying Conditional Causal Effects CoRR (2012)
Changsung Kang, Jin Tian
Local Markov Property for Models Satisfying Composition Axiom CoRR (2012)
Jin Tian
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement CoRR (2012)
Jin Tian, Ru He
Computing Posterior Probabilities of Structural Features in Bayesian Networks CoRR (2012)
Jin Tian, Ru He, Lavanya Ram
Bayesian Model Averaging Using the k-best Bayesian Network Structures CoRR (2012)
Jin Tian
Identifying Dynamic Sequential Plans CoRR (2012)
Jin Tian
A Criterion for Parameter Identification in Structural Equation Models CoRR (2012)
Changsung Kang, Jin Tian
Inequality Constraints in Causal Models with Hidden Variables. CoRR (2012)
Changsung Kang, Jin Tian
Polynomial Constraints in Causal Bayesian Networks CoRR (2012)
Jin Tian, Ru He, Lavanya Ram
Bayesian Model Averaging Using the k-best Bayesian Network Structures. UAI (2010)
Jin Tian, Ru He
Computing Posterior Probabilities of Structural Features in Bayesian Networks. UAI (2009)
Jin Tian
Parameter Identification in a Class of Linear Structural Equation Models. IJCAI (2009)
Changsung Kang, Jin Tian
Markov Properties for Linear Causal Models with Correlated Errors. J. Mach. Learn. Res. 10 (2009)
Jin Tian
Identifying Dynamic Sequential Plans. UAI (2008)
Changsung Kang, Jin Tian
Polynomial Constraints in Causal Bayesian Networks. UAI (2007)
Jin Tian
A Criterion for Parameter Identification in Structural Equation Models. UAI (2007)
Jin Tian
On the Identification of a Class of Linear Models. AAAI (2007)
Changsung Kang, Jin Tian
A Hybrid Generative/Discriminative Bayesian Classifier. FLAIRS Conference (2006)
Changsung Kang, Jin Tian
Inequality Constraints in Causal Models with Hidden Variables. UAI (2006)
Jin Tian, Changsung Kang, Judea Pearl
A Characterization of Interventional Distributions in Semi-Markovian Causal Models. AAAI (2006)
Jin Tian
Identifying Direct Causal Effects in Linear Models. AAAI (2005)
Jin Tian
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement. UAI (2005)
Changsung Kang, Jin Tian
Local Markov Property for Models Satisfying Composition Axiom. UAI (2005)
Jin Tian
Identifying Linear Causal Effects. AAAI (2004)
Jin Tian
Identifying Conditional Causal Effects. UAI (2004)
Jin Tian, Judea Pearl
A New Characterization of the Experimental Implications of Causal Bayesian Networks. AAAI/IAAI (2002)
Jin Tian, Judea Pearl
On the Testable Implications of Causal Models with Hidden Variables. UAI (2002)
Jin Tian, Judea Pearl
A General Identification Condition for Causal Effects. AAAI/IAAI (2002)
Jin Tian, Judea Pearl
Causal Discovery from Changes. UAI (2001)
Jin Tian
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks. UAI (2000)
Jin Tian, Judea Pearl
Probabilities of causation: Bounds and identification. Ann. Math. Artif. Intell. 28 (1-4) (2000)
Jin Tian, Judea Pearl
Probabilities of Causation: Bounds and Identification. UAI (2000)