Causal discovery in multiple models from different experiments. - Tom Claassen; Tom Heskes (NIPS, 2010)

A structure independent algorithm for causal discovery. - Tom Claassen; Tom Heskes (ESANN, 2011)

A Logical Characterization of Constraint-Based Causal Discovery. - Tom Claassen; Tom Heskes (UAI, 2011)

A Bayesian Approach to Constraint Based Causal Inference. - Tom Claassen; Tom Heskes (UAI, 2012)

Bayesian Probabilities for Constraint-Based Causal Discovery. - Tom Claassen; Tom Heskes (IJCAI, 2013)

Learning Sparse Causal Models is not NP-hard. - Tom Claassen; Joris M. Mooij; Tom Heskes (UAI, 2013)

Causal Discovery from Databases with Discrete and Continuous Variables. - Elena Sokolova; Perry Groot; Tom Claassen; Tom Heskes (Probabilistic Graphical Models, 2014)

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 (AIME, 2015)

Ancestral Causal Inference. - Sara Magliacane; Tom Claassen; Joris M. Mooij (NIPS, 2016)

Computing Lower and Upper Bounds on the Probability of Causal Statements. - Elena Sokolova; Martine Hoogman; Perry Groot; Tom Claassen; Tom Heskes (Probabilistic Graphical Models, 2016)

Joint Causal Inference on Observational and Experimental Datasets. - Sara Magliacane; Tom Claassen; Joris M. Mooij (CoRR, 2016)

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (AISTATS, 2017)

Causal Transfer Learning. - Sara Magliacane; Thijs van Ommen; Tom Claassen; Stephan Bongers; Philip Versteeg; Joris M. Mooij (CoRR, 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)

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 (NeurIPS, 2018)

A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. - Ioan Gabriel Bucur; Tom van Bussel; Tom Claassen; Tom Heskes (PGM, 2018)

Large-scale local causal inference of gene regulatory relationships. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (Int. J. Approx. Reason., 2019)

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. - Tom Heskes; Evi Sijben; Ioan Gabriel Bucur; Tom Claassen (NeurIPS, 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 (PGM, 2020)

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (UAI, 2020)

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. - Joris M. Mooij; Tom Claassen (UAI, 2020)

Constraint-Based Causal Discovery In The Presence Of Cycles. - Joris M. Mooij; Tom Claassen (CoRR, 2020)

Joint Causal Inference from Multiple Contexts. - Joris M. Mooij; Sara Magliacane; Tom Claassen (J. Mach. Learn. Res., 2020)

Spectral Ranking of Causal Influence in Complex Systems. - Errol Zalmijn; Tom Heskes; Tom Claassen (Entropy, 2021)

Greedy equivalence search in the presence of latent confounders. - Tom Claassen; Ioan Gabriel Bucur (UAI, 2022)

Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. - Mirthe Maria Van Diepen; Ioan Gabriel Bucur; Tom Heskes; Tom Claassen (CLeaR, 2023)

Establishing Markov equivalence in cyclic directed graphs. - Tom Claassen; Joris M. Mooij (UAI, 2023)

AutoCD: Automated Machine Learning for Causal Discovery Algorithms. - Gerlise Chan; Tom Claassen; Holger H. Hoos; Tom Heskes; Mitra Baratchi (PGM, 2024)

Towards a Benchmark for Scientific Understanding in Humans and Machines. - Kristian Gonzalez Barman; Sascha Caron; Tom Claassen; Henk W. de Regt (Minds Mach., 2024)

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph. - Mátyás Schubert; Tom Claassen; Sara Magliacane (AISTATS, 2025)