Tom Claassen

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 29

Top Topics

Causal Discovery

Observational Data

Gene Regulatory

Top Venues

Probabilistic Graphical Models

Publications

Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt
Towards a Benchmark for Scientific Understanding in Humans and Machines. Minds Mach. 34 (1) (2024)
Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR (2023)
Tom Claassen, Joris M. Mooij
Establishing Markov Equivalence in Cyclic Directed Graphs. CoRR (2023)
Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt
Towards a Benchmark for Scientific Understanding in Humans and Machines. CoRR (2023)
Tom Claassen, Joris M. Mooij
Establishing Markov equivalence in cyclic directed graphs. UAI (2023)
Tom Claassen, Ioan Gabriel Bucur
Greedy equivalence search in the presence of latent confounders. UAI (2022)
Errol Zalmijn, Tom Heskes, Tom Claassen
Spectral Ranking of Causal Influence in Complex Systems. Entropy 23 (3) (2021)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI (2020)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR (2020)
Joris M. Mooij, Tom Claassen
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. UAI (2020)
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR (2020)
Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes
Discovering cause-effect relationships in spatial systems with a known direction based on observational data. PGM (2020)
Errol Zalmijn, Tom Heskes, Tom Claassen
Spectral Ranking of Causal Influence in Complex Systems. CoRR (2020)
Joris M. Mooij, Sara Magliacane, Tom Claassen
Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. 21 (2020)
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS (2020)
Joris M. Mooij, Tom Claassen
Constraint-Based Causal Discovery In The Presence Of Cycles. CoRR (2020)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. 115 (2019)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR (2019)
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. NeurIPS (2018)
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM (2018)
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR (2018)
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Causal Transfer Learning. CoRR (2017)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR (2017)
Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes
Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. 3 (2) (2017)
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS (2017)
Sara Magliacane, Tom Claassen, Joris M. Mooij
Joint Causal Inference on Observational and Experimental Datasets. CoRR (2016)
Sara Magliacane, Tom Claassen, Joris M. Mooij
Ancestral Causal Inference. NIPS (2016)
Sara Magliacane, Tom Claassen, Joris M. Mooij
Ancestral Causal Inference. CoRR (2016)
Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes
Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models (2016)
Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes
Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME (2015)
Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes
Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models (2014)
Tom Claassen, Tom Heskes
Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI (2013)
Tom Claassen, Joris M. Mooij, Tom Heskes
Learning Sparse Causal Models is not NP-hard. UAI (2013)
Tom Claassen, Joris M. Mooij, Tom Heskes
Learning Sparse Causal Models is not NP-hard. CoRR (2013)
Tom Claassen, Tom Heskes
A Bayesian Approach to Constraint Based Causal Inference CoRR (2012)
Tom Claassen, Tom Heskes
A Bayesian Approach to Constraint Based Causal Inference. UAI (2012)
Tom Claassen, Tom Heskes
A Logical Characterization of Constraint-Based Causal Discovery CoRR (2012)
Tom Claassen, Tom Heskes
A structure independent algorithm for causal discovery. ESANN (2011)
Tom Claassen, Tom Heskes
A Logical Characterization of Constraint-Based Causal Discovery. UAI (2011)
Tom Claassen, Tom Heskes
Causal discovery in multiple models from different experiments. NIPS (2010)