Antti Hyttinen

Michael Mathioudakis

Proceedings of the Discovery Science - 23rd International Conference, 2020

A Bayesian Approach for Estimating Causal Effects from Observational Data.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

A logical approach to context-specific independence.

[BibT_eX]

[DOI]

Ann. Pure Appl. Log., 2019

Identifying Causal Effects via Context-specific Independence Relations.

[BibT_eX]

[DOI]

Santtu Tikka

Juha Karvanen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Applications of MaxSAT in Data Analysis.

[BibT_eX]

[DOI]

Jeremias Berg

Proceedings of Pragmatics of SAT 2015, 2018

Learning Optimal Causal Graphs with Exact Search.

[BibT_eX]

[DOI]

Kari Rantanen

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Structure Learning for Bayesian Networks over Labeled DAGs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Reduced Cost Fixing for Maximum Satisfiability.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

A constraint optimization approach to causal discovery from subsampled time series data.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

Learning Chordal Markov Networks via Branch and Bound.

[BibT_eX]

[DOI]

Kari Rantanen

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Core-Guided Approach to Learning Optimal Causal Graphs.

[BibT_eX]

[DOI]

Paul Saikko

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Reduced Cost Fixing in MaxSAT.

[BibT_eX]

[DOI]

Proceedings of the Principles and Practice of Constraint Programming, 2017

Advanced Methodologies for Bayesian Networks 2017: Preface.

[BibT_eX]

[DOI]

Joe Suzuki

Brandon M. Malone

Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

2016

Causal Discovery from Subsampled Time Series Data by Constraint Optimization.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015

Learning Optimal Chain Graphs with Answer Set Programming.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Do-calculus when the True Graph Is Unknown.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

2013

Discovering Causal Relations in the Presence of Latent Confounders.

[BibT_eX]

[DOI]

PhD thesis, 2013

Experiment selection for causal discovery.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2013

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012

Learning linear cyclic causal models with latent variables.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2012

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

Noisy-OR Models with Latent Confounding.

[BibT_eX]

[DOI]

Proceedings of the UAI 2011, 2011

2009

Bayesian Discovery of Linear Acyclic Causal Models.

[BibT_eX]

[DOI]