Jin Tian

发表

On the Testable Implications of Causal Models with Hidden Variables pdf

Jin Tian, Judea Pearl, J. Pearl, 2002, UAI.

Bounds on Direct Effects in the Presence of Confounded Intermediate Variables

J. Pearl, Manabu Kuroki, Zhihong Cai, 2008, Biometrics.

Estimating Identifiable Causal Effects through Double Machine Learning

Jin Tian, Elias Bareinboim, Yonghan Jung, 2021, AAAI.

Learning Bayesian network structures under incremental construction curricula

Kewei Tu, Jin Tian, Yanpeng Zhao, 2017, Neurocomputing.

Curriculum Learning of Bayesian Network Structures

Kewei Tu, Jin Tian, Yanpeng Zhao, 2015, ACML.

Graphical Models for Inference with Missing Data

Jin Tian, Kilian Q. Weinberger, Judea Pearl, 2013, NIPS.

Causal Discovery from Changes pdf

Jin Tian, Judea Pearl, J. Pearl, 2001, UAI.

A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks

Jin Tian, Jin Tian, 2000, UAI.

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling

Jin Tian, Huaiqing Wu, Ru He, 2015, J. Mach. Learn. Res..

A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks pdf

Srinivas Aluru, Jin Tian, Yetian Chen, 2014, ArXiv.

Structure learning in Bayesian networks and session analysis of people search within a professional social network

Jin Tian, Jiong Wang, Igor Perisic, 2014 .

Recovering from Selection Bias in Causal and Statistical Inference

Jin Tian, Elias Bareinboim, Judea Pearl, 2014, AAAI.

Markov Properties for Linear Causal Models with Correlated Errors

Jin Tian, Changsung Kang, Jin Tian, 2009, J. Mach. Learn. Res..

Testable Implications of Linear Structural Equation Models

Jin Tian, Judea Pearl, Bryant Chen, 2014, AAAI.

Probabilities of causation: Bounds and identification

Jin Tian, Judea Pearl, J. Pearl, 2000, Annals of Mathematics and Artificial Intelligence.

A general identification condition for causal effects

Jin Tian, Judea Pearl, J. Pearl, 2002, AAAI/IAAI.

Generalized Adjustment Under Confounding and Selection Biases

Jin Tian, Elias Bareinboim, Juan D. Correa, 2018, AAAI.

Learning Causal Effects via Weighted Empirical Risk Minimization

Jin Tian, Elias Bareinboim, Yonghan Jung, 2020, NeurIPS.

Parameter Identification in a Class of Linear Structural Equation Models

Jin Tian, Jin Tian, 2009, IJCAI.

On the Identification of a Class of Linear Models

Jin Tian, Jin Tian, 2007, AAAI.

A Criterion for Parameter Identification in Structural Equation Models

Jin Tian, Jin Tian, 2007, UAI.

Identifying Direct Causal Effects in Linear Models

Jin Tian, Jin Tian, 2005, AAAI.

Identifying Linear Causal Effects

Jin Tian, Jin Tian, 2004, AAAI.

Missing at Random in Graphical Models

Jin Tian, Jin Tian, 2015, AISTATS.

Identifying Dynamic Sequential Plans

Jin Tian, Jin Tian, 2008, UAI.

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

Jin Tian, Elias Bareinboim, Yonghan Jung, 2021, ICML.

Instrumental Variable Estimation of Average Partial Causal Effects

Manabu Kuroki, Jin Tian, Yuta Kawakami, 2023, ICML.

Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging

Jin Tian, Yetian Chen, 2014, AAAI.

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks

Jin Tian, Yetian Chen, Lingjian Meng, 2015, AISTATS.

Bayesian model averaging using the k -best Bayesian network structures pdf

Jin Tian, Ru He, Lavanya Ram, 2010, UAI 2010.

Computing Posterior Probabilities of Structural Features in Bayesian Networks pdf

Jin Tian, Ru He, Jin Tian, 2009, UAI.

A new characterization of the experimental implications of causal Bayesian networks

Jin Tian, Judea Pearl, J. Pearl, 2002, AAAI/IAAI.

Adjustment Criteria for Generalizing Experimental Findings

Jin Tian, Elias Bareinboim, Juan D. Correa, 2019, ICML.

Recovering Causal Effects from Selection Bias

Jin Tian, Elias Bareinboim, E. Bareinboim, 2015, AAAI.

Probabilities of causation: Bounds and identification

J. Pearl, Jin Tian, 2000, Annals of Mathematics and Artificial Intelligence.

Identifying Conditional Causal Effects

Jin Tian, Jin Tian, 2004, UAI.

Title Bounds on Direct Effect in the Presence of Confounded Intermediate Variables Permalink

J. Pearl, Manabu Kuroki, Zhihong Cai, 2008 .

Inequality Constraints in Causal Models with Hidden Variables pdf

Jin Tian, Changsung Kang, Jin Tian, 2006, UAI.

Recovering Probability Distributions from Missing Data

Jin Tian, Jin Tian, 2017, ACML.

Recoverability of Joint Distribution from Missing Data pdf

Jin Tian, Jin Tian, 2016, ArXiv.

A Characterization of Interventional Distributions in Semi-Markovian Causal Models

Jin Tian, Judea Pearl, Changsung Kang, 2006, AAAI.

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling

Jin Tian, Huaiqin Wu, Ru He, 2015, J. Mach. Learn. Res..

Bayesian Model Averaging Using the k-best Bayesian Network Structures

Jin Tian, Ru He, Lavanya Ram, 2012, UAI.

Polynomial Constraints in Causal Bayesian Networks pdf

Jin Tian, Changsung Kang, Jin Tian, 2007, UAI.

Local Markov Property for Models Satisfying Composition Axiom

Jin Tian, Changsung Kang, Jin Tian, 2005, UAI.

Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement pdf

Jin Tian, Jin Tian, 2005, UAI.