Jiji Zhang

Orcid: 0000-0003-0684-2084

According to our database1, Jiji Zhang authored at least 46 papers between 2003 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
On Low-Rank Directed Acyclic Graphs and Causal Structure Learning.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

Natural Counterfactuals With Necessary Backtracking.
CoRR, 2024

2023
Local Search for Efficient Causal Effect Estimation.
IEEE Trans. Knowl. Data Eng., September, 2023

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective.
ACM Comput. Surv., 2023

A Characterization of Lewisian Causal Models.
Proceedings of the Logic, Rationality, and Interaction - 9th International Workshop, 2023

2022
Pathway to Future Symbiotic Creativity.
CoRR, 2022

Markov categories, causal theories, and the do-calculus.
CoRR, 2022

Ancestral instrument method for causal inference without a causal graph.
CoRR, 2022

Reframed GES with a neural conditional dependence measure.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Ancestral Instrument Method for Causal Inference without Complete Knowledge.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Subjective causal networks and indeterminate suppositional credences.
Synth., 2021

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Causal Discovery from Heterogeneous/Nonstationary Data.
J. Mach. Learn. Res., 2020

On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption.
Proceedings of the Algorithmic Learning Theory, 2020

2019
Identification of Conditional Causal Effects under Markov Equivalence.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

On Causal Identification under Markov Equivalence.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Causal Identification under Markov Equivalence: Completeness Results.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Agreeing to disagree and dilation.
Int. J. Approx. Reason., 2018

How to Tackle an Extremely Hard Learning Problem: Learning Causal Structures from Non-Experimental Data without the Faithfulness Assumption or the Like.
CoRR, 2018

A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017
Weakening faithfulness: some heuristic causal discovery algorithms.
Int. J. Data Sci. Anal., 2017

SAT-Based Causal Discovery under Weaker Assumptions.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

2016
On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model.
ACM Trans. Intell. Syst. Technol., 2016

The three faces of faithfulness.
Synth., 2016

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2015
Distinguishing Cause from Effect Based on Exogeneity.
CoRR, 2015

2013
A Lewisian Logic of Causal Counterfactuals.
Minds Mach., 2013

A Peculiarity in Pearl's Logic of Interventionist Counterfactuals.
J. Philos. Log., 2013

2011
Intervention, determinism, and the causal minimality condition.
Synth., 2011

Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables".
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010
Actual causation: a stone soup essay.
Synth., 2010

2008
Error probabilities for inference of causal directions.
Synth., 2008

Detection of Unfaithfulness and Robust Causal Inference.
Minds Mach., 2008

Causal Reasoning with Ancestral Graphs.
J. Mach. Learn. Res., 2008

On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias.
Artif. Intell., 2008

2007
Generalized Do-Calculus with Testable Causal Assumptions.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.
Proceedings of the UAI 2007, 2007

2006
Adjacency-Faithfulness and Conservative Causal Inference.
Proceedings of the UAI '06, 2006

2005
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables.
Proceedings of the UAI '05, 2005

Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.
Proceedings of the UAI '05, 2005

Probabilistic workflow mining.
Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

2003
Strong Faithfulness and Uniform Consistency in Causal Inference.
Proceedings of the UAI '03, 2003


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