Joris M. Mooij

According to our database, Joris M. Mooij authored at least 67 papers between 2004 and 2024.

Collaborative distances:

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

2024

Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift.

Philip A. Boeken, Onno Zoeter, Joris M. Mooij

CoRR, 2024

Modeling Latent Selection with Structural Causal Models.

Leihao Chen, Onno Zoeter, Joris M. Mooij

CoRR, 2024

2023

Establishing Markov equivalence in cyclic directed graphs.

Tom Claassen, Joris M. Mooij

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Correcting for selection bias and missing response in regression using privileged information.

Philip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Robustness of model predictions under extension.

Tineke Blom, Joris M. Mooij

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Local Constraint-Based Causal Discovery under Selection Bias.

Philip Versteeg, Joris M. Mooij, Cheng Zhang

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Causal Bandits without prior knowledge using separating sets.

Arnoud A. W. M. de Kroon, Joris M. Mooij, Danielle Belgrave

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Conditional independences and causal relations implied by sets of equations.

Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij

J. Mach. Learn. Res., 2021

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions.

Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij

CoRR, 2021

Causality and independence in perfectly adapted dynamical systems.

Tineke Blom, Joris M. Mooij

CoRR, 2021

A weaker faithfulness assumption based on triple interactions.

Alexander Marx, Arthur Gretton, Joris M. Mooij

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery.

Philip A. Boeken, Joris M. Mooij

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020

Joint Causal Inference from Multiple Contexts.

Joris M. Mooij, Sara Magliacane, Tom Claassen

J. Mach. Learn. Res., 2020

Causal Discovery for Causal Bandits utilizing Separating Sets.

Arnoud A. W. M. de Kroon, Danielle Belgrave, Joris M. Mooij

CoRR, 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

2019

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias.

Patrick Forré, Joris M. Mooij

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Beyond Structural Causal Models: Causal Constraints Models.

Tineke Blom, Stephan Bongers, Joris M. Mooij

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Boosting Local Causal Discovery in High-Dimensional Expression Data.

Philip Versteeg, Joris M. Mooij

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

2018

An Upper Bound for Random Measurement Error in Causal Discovery.

Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

CoRR, 2018

Algebraic Equivalence of Linear Structural Equation Models.

Thijs van Ommen, Joris M. Mooij

CoRR, 2018

Generalized Strucutral Causal Models.

Tineke Blom, Joris M. Mooij

CoRR, 2018

From Random Differential Equations to Structural Causal Models: the stochastic case.

Stephan Bongers, Joris M. Mooij

CoRR, 2018

From Deterministic ODEs to Dynamic Structural Causal Models.

Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders.

Patrick Forré, Joris M. Mooij

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Causal Discovery in the Presence of Measurement Error.

Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

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

Causal Transfer Learning.

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

CoRR, 2017

Causal Consistency of Structural Equation Models.

Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Algebraic Equivalence Class Selection for Linear Structural Equation Models.

Thijs van Ommen, Joris M. Mooij

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Causal Effect Inference with Deep Latent-Variable Models.

Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling

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

2016

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.

Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

J. Mach. Learn. Res., 2016

Joint Causal Inference on Observational and Experimental Datasets.

Sara Magliacane, Tom Claassen, Joris M. Mooij

CoRR, 2016

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions.

Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij

CoRR, 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

MAGMA: Generalized Gene-Set Analysis of GWAS Data.

Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma

PLoS Comput. Biol., 2015

An Empirical Study of the Simplest Causal Prediction Algorithm.

Jerome Cremers, Joris M. Mooij

Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), 2015

2014

Causal discovery with continuous additive noise models.

Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

J. Mach. Learn. Res., 2014

Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example.

Nicholas Cornia, Joris M. Mooij

Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), 2014

2013

From Ordinary Differential Equations to Structural Causal Models: the deterministic case.

Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

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

Cyclic Causal Discovery from Continuous Equilibrium Data.

Joris M. Mooij, Tom Heskes

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

Semi-supervised Learning in Causal and Anticausal Settings.

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012

Information-geometric approach to inferring causal directions.

Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf

Artif. Intell., 2012

On causal and anticausal learning.

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller.

Suzanna Martens, Joris M. Mooij, N. Jeremy Hill, Jason Farquhar, Bernhard Schölkopf

Neural Comput., 2011

Identifiability of Causal Graphs using Functional Models.

Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

Proceedings of the UAI 2011, 2011

Efficient inference in matrix-variate Gaussian models with \iid observation noise.

Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

On Causal Discovery with Cyclic Additive Noise Models.

Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Learning of causal relations.

John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl

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

2010

Remote Sensing Feature Selection by Kernel Dependence Measures.

Gustavo Camps-Valls, Joris M. Mooij, Bernhard Schölkopf

IEEE Geosci. Remote. Sens. Lett., 2010

Distinguishing between cause and effect.

Joris M. Mooij, Dominik Janzing

Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models.

Joris M. Mooij

J. Mach. Learn. Res., 2010

Inferring deterministic causal relations.

Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf

Proceedings of the UAI 2010, 2010

Probabilistic latent variable models for distinguishing between cause and effect.

Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf

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

2009

Identifying confounders using additive noise models.

Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf

Proceedings of the UAI 2009, 2009

Regression by dependence minimization and its application to causal inference in additive noise models.

Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Bounds on marginal probability distributions.

Joris M. Mooij, Hilbert J. Kappen

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Nonlinear causal discovery with additive noise models.

Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Sufficient Conditions for Convergence of the Sum-Product Algorithm.

Joris M. Mooij, Hilbert J. Kappen

IEEE Trans. Inf. Theory, 2007

Loop Corrected Belief Propagation.

Joris M. Mooij, Bastian Wemmenhove, Bert Kappen, Tommaso Rizzo

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Loop Corrections for Approximate Inference on Factor Graphs.

Joris M. Mooij, Hilbert J. Kappen

J. Mach. Learn. Res., 2007

Truncating the Loop Series Expansion for Belief Propagation.

Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen

J. Mach. Learn. Res., 2007

Inference in the Promedas Medical Expert System.

Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn A. R. Leisink, Hilbert J. Kappen, Jan P. Neijt

Proceedings of the Artificial Intelligence in Medicine, 2007

2006

Loop corrections for approximate inference

Joris M. Mooij, Bert Kappen

CoRR, 2006

2005

Sufficient Conditions for Convergence of Loopy Belief Propagation.

Joris M. Mooij, Hilbert J. Kappen

Proceedings of the UAI '05, 2005

2004

Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks.

Joris M. Mooij, Hilbert J. Kappen

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004