A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks. - Jin Tian 001 (UAI, 2000)

Probabilities of causation: Bounds and identification. - Jin Tian 001; Judea Pearl (Ann. Math. Artif. Intell., 2000)

Causal Discovery from Changes. - Jin Tian 001; Judea Pearl (UAI, 2001)

A General Identification Condition for Causal Effects. - Jin Tian 001; Judea Pearl (AAAI/IAAI, 2002)

A New Characterization of the Experimental Implications of Causal Bayesian Networks. - Jin Tian 001; Judea Pearl (AAAI/IAAI, 2002)

On the Testable Implications of Causal Models with Hidden Variables. - Jin Tian 001; Judea Pearl (UAI, 2002)

Identifying Linear Causal Effects. - Jin Tian 001 (AAAI, 2004)

Identifying Conditional Causal Effects. - Jin Tian 001 (UAI, 2004)

Identifying Direct Causal Effects in Linear Models. - Jin Tian 001 (AAAI, 2005)

Local Markov Property for Models Satisfying Composition Axiom. - Changsung Kang; Jin Tian 001 (UAI, 2005)

Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement. - Jin Tian 001 (UAI, 2005)

A Characterization of Interventional Distributions in Semi-Markovian Causal Models. - Jin Tian 001; Changsung Kang; Judea Pearl (AAAI, 2006)

A Hybrid Generative/Discriminative Bayesian Classifier. - Changsung Kang; Jin Tian 001 (FLAIRS, 2006)

Inequality Constraints in Causal Models with Hidden Variables. - Changsung Kang; Jin Tian 001 (UAI, 2006)

On the Identification of a Class of Linear Models. - Jin Tian 001 (AAAI, 2007)

Polynomial Constraints in Causal Bayesian Networks. - Changsung Kang; Jin Tian 001 (UAI, 2007)

A Criterion for Parameter Identification in Structural Equation Models. - Jin Tian 001 (UAI, 2007)

Identifying Dynamic Sequential Plans. - Jin Tian 001 (UAI, 2008)

Parameter Identification in a Class of Linear Structural Equation Models. - Jin Tian 001 (IJCAI, 2009)

Computing Posterior Probabilities of Structural Features in Bayesian Networks. - Jin Tian 001; Ru He (UAI, 2009)

Markov Properties for Linear Causal Models with Correlated Errors. - Changsung Kang; Jin Tian 001 (J. Mach. Learn. Res., 2009)

Bayesian Model Averaging Using the k-best Bayesian Network Structures. - Jin Tian 001; Ru He; Lavanya Ram (UAI, 2010)

Graphical Models for Inference with Missing Data. - Karthika Mohan; Judea Pearl; Jin Tian 001 (NIPS, 2013)

Session analysis of people search within a professional social network. - Ru He; Jiong Wang; Jin Tian 001; Cheng-Tao Chu; Bradley Mauney; Igor Perisic (J. Assoc. Inf. Sci. Technol., 2013)

Recovering from Selection Bias in Causal and Statistical Inference. - Elias Bareinboim; Jin Tian 001; Judea Pearl (AAAI, 2014)

Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging. - Yetian Chen; Jin Tian 001 (AAAI, 2014)

Testable Implications of Linear Structural Equation Models. - Bryant Chen; Jin Tian 001; Judea Pearl (AAAI, 2014)

A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks. - Yetian Chen; Jin Tian 001; Olga Nikolova; Srinivas Aluru (CoRR, 2014)

Recovering Causal Effects from Selection Bias. - Elias Bareinboim; Jin Tian 001 (AAAI, 2015)

Curriculum Learning of Bayesian Network Structures. - Yanpeng Zhao; Yetian Chen; Kewei Tu; Jin Tian 001 (ACML, 2015)

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks. - Yetian Chen; Lingjian Meng; Jin Tian 001 (AISTATS, 2015)

Missing at Random in Graphical Models. - Jin Tian 001 (AISTATS, 2015)

Joint Discovery of Skill Prerequisite Graphs and Student Models. - Yetian Chen; José P. González-Brenes; Jin Tian 001 (EDM, 2016)

Recoverability of Joint Distribution from Missing Data. - Jin Tian 001 (CoRR, 2016)

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling. - Ru He; Jin Tian 001; Huaiqing Wu (J. Mach. Learn. Res., 2016)

Recovering Probability Distributions from Missing Data. - Jin Tian 001 (ACML, 2017)

Learning Bayesian network structures under incremental construction curricula. - Yanpeng Zhao; Yetian Chen; Kewei Tu; Jin Tian 001 (Neurocomputing, 2017)

Generalized Adjustment Under Confounding and Selection Biases. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (AAAI, 2018)

Identification of Causal Effects in the Presence of Selection Bias. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (AAAI, 2019)

Adjustment Criteria for Generalizing Experimental Findings. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (ICML, 2019)

Adjustment Criteria for Recovering Causal Effects from Missing Data. - Mojdeh Saadati; Jin Tian 001 (ECML/PKDD (1), 2019)

Event prediction algorithm using neural networks for the power management system of electric vehicles. - Ki-sung Koo; Manimaran Govindarasu; Jin Tian 001 (Appl. Soft Comput., 2019)

Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders. - Hebi Li; Qi Xiao; Shixin Tian; Jin Tian 001 (CoRR, 2019)

Estimating Causal Effects Using Weighting-Based Estimators. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (AAAI, 2020)

Learning Causal Effects via Weighted Empirical Risk Minimization. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (NeurIPS, 2020)

Supervised Whole DAG Causal Discovery. - Hebi Li; Qi Xiao; Jin Tian 001 (CoRR, 2020)

Estimating Identifiable Causal Effects through Double Machine Learning. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (AAAI, 2021)

BHDL: A Lucid, Expressive, and Embedded Programming Language and System for PCB Designs. - Hebi Li; Youbiao He; Qi Xiao; Jin Tian 001; Forrest Sheng Bao (DAC, 2021)

An Improved (Adversarial) Reprogramming Technique for Neural Networks. - Eliska Kloberdanz; Jin Tian 001; Wei Le (ICANN (1), 2021)

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (ICML, 2021)

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (NeurIPS, 2021)

Data Poisoning Attacks and Defenses to Crowdsourcing Systems. - Minghong Fang; Minghao Sun; Qi Li 012; Neil Zhenqiang Gong; Jin Tian 001; Jia Liu 002 (WWW, 2021)

Recovering from Selection Bias in Causal and Statistical Inference. - Elias Bareinboim; Jin Tian 001; Judea Pearl (Probabilistic and Causal Inference, 2022)

Group-Wise Feature Selection for Supervised Learning. - Qi Xiao; Hebi Li; Jin Tian 001; Zhengdao Wang (ICASSP, 2022)

On Measuring Causal Contributions via do-interventions. - Yonghan Jung; Shiva Prasad Kasiviswanathan; Jin Tian 001; Dominik Janzing; Patrick Blöbaum; Elias Bareinboim (ICML, 2022)

Partial Counterfactual Identification from Observational and Experimental Data. - Junzhe Zhang 001; Jin Tian 001; Elias Bareinboim (ICML, 2022)

Finding and Listing Front-door Adjustment Sets. - Hyunchai Jeong; Jin Tian 001; Elias Bareinboim (NeurIPS, 2022)

Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning. - Youbiao He; Hebi Li; Jin Tian 001; Forrest Sheng Bao (VLSI-DAT, 2022)

Effect Identification in Cluster Causal Diagrams. - Tara V. Anand; Adèle H. Ribeiro; Jin Tian 001; Elias Bareinboim (CoRR, 2022)

Causal Effect Identification in Cluster DAGs. - Tara V. Anand; Adèle H. Ribeiro; Jin Tian 001; Elias Bareinboim (AAAI, 2023)

Estimating Joint Treatment Effects by Combining Multiple Experiments. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (ICML, 2023)

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. - Yonghan Jung; Ivan Diaz; Jin Tian 001; Elias Bareinboim (NeurIPS, 2023)

Codepod: A Namespace-Aware, Hierarchical Jupyter for Interactive Development at Scale. - Hebi Li; Forrest Sheng Bao; Qi Xiao; Jin Tian 001 (CoRR, 2023)

Unified Covariate Adjustment for Causal Inference. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (NeurIPS, 2024)

AnnotatedTables: A Large Tabular Dataset with Language Model Annotations. - Yaojie Hu; Ilias Fountalis; Jin Tian 001; Nikolaos Vasiloglou (CoRR, 2024)

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. - Hyunchai Jeong; Adiba Ejaz; Jin Tian 001; Elias Bareinboim (AAAI, 2025)

Scalable Tabular Dataset Labeling with Language Models. - Yaojie Hu; Ilias Fountalis; Jin Tian 001; Nikolaos Vasiloglou (SSDBM, 2025)