Tom Claassen

According to our database, Tom Claassen authored at least 28 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Towards a Benchmark for Scientific Understanding in Humans and Machines.

Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt

Minds Mach., February, 2024

2023

Establishing Markov equivalence in cyclic directed graphs.

Tom Claassen, Joris M. Mooij

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding.

Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

Greedy equivalence search in the presence of latent confounders.

Tom Claassen, Ioan Gabriel Bucur

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Spectral Ranking of Causal Influence in Complex Systems.

Errol Zalmijn, Tom Heskes, Tom Claassen

Entropy, 2021

2020

Joint Causal Inference from Multiple Contexts.

Joris M. Mooij, Sara Magliacane, Tom Claassen

J. Mach. Learn. Res., 2020

Constraint-Based Causal Discovery In The Presence Of Cycles.

Joris M. Mooij, Tom Claassen

CoRR, 2020

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles.

Joris M. Mooij, Tom Claassen

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models.

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Discovering cause-effect relationships in spatial systems with a known direction based on observational data.

Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models.

Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Large-scale local causal inference of gene regulatory relationships.

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Int. J. Approx. Reason., 2019

2018

A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks.

Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions.

Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD.

Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes

Int. J. Data Sci. Anal., 2017

Causal Transfer Learning.

Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij

CoRR, 2017

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness.

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Joint Causal Inference on Observational and Experimental Datasets.

Sara Magliacane, Tom Claassen, Joris M. Mooij

CoRR, 2016

Computing Lower and Upper Bounds on the Probability of Causal Statements.

Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes

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

Ancestral Causal Inference.

Sara Magliacane, Tom Claassen, Joris M. Mooij

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data.

Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes

Proceedings of the Artificial Intelligence in Medicine, 2015

2014

Causal Discovery from Databases with Discrete and Continuous Variables.

Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

2013

Learning Sparse Causal Models is not NP-hard.

Tom Claassen, Joris M. Mooij, Tom Heskes

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

Bayesian Probabilities for Constraint-Based Causal Discovery.

Tom Claassen, Tom Heskes

Proceedings of the IJCAI 2013, 2013

2012

A Bayesian Approach to Constraint Based Causal Inference.

Tom Claassen, Tom Heskes

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

2011

A Logical Characterization of Constraint-Based Causal Discovery.

Tom Claassen, Tom Heskes

Proceedings of the UAI 2011, 2011

A structure independent algorithm for causal discovery.

Tom Claassen, Tom Heskes

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

2010

Causal discovery in multiple models from different experiments.

Tom Claassen, Tom Heskes

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010