Joris M. Mooij

100 papers A* 3A 24B 2Journal 52Unranked 17

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses. UAI Sourbh Bhadane, Joris M. Mooij, Philip A. Boeken, Onno Zoeter
2025	J	`jnl`	Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses. CoRR Sourbh Bhadane, Joris M. Mooij, Philip A. Boeken, Onno Zoeter
2025	—	`conf`	Robust Multi-view Co-expression Network Inference. CLeaR Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré
2025	A	`conf`	σ-Maximal Ancestral Graphs. UAI Binghua Yao, Joris M. Mooij
2025	J	`jnl`	σ-Maximal Ancestral Graphs. CoRR Binghua Yao, Joris M. Mooij
2024	—	`conf`	Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. CLeaR Philip A. Boeken, Onno Zoeter, Joris M. Mooij
2024	J	`jnl`	Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. CoRR Philip A. Boeken, Onno Zoeter, Joris M. Mooij
2024	J	`jnl`	Modeling Latent Selection with Structural Causal Models. CoRR Leihao Chen, Onno Zoeter, Joris M. Mooij
2024	J	`jnl`	Robust Multi-view Co-expression Network Inference. CoRR Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré
2024	A	`ed.`	Uncertainty in Artificial Intelligence, 15-19 July 2024, Universitat Pompeu Fabra, Barcelona, Spain. UAI Negar Kiyavash, Joris M. Mooij
2023	A	`conf`	Correcting for selection bias and missing response in regression using privileged information. UAI Philip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter
2023	J	`jnl`	Establishing Markov Equivalence in Cyclic Directed Graphs. CoRR Tom Claassen, Joris M. Mooij
2023	A	`conf`	Establishing Markov equivalence in cyclic directed graphs. UAI Tom Claassen, Joris M. Mooij
2022	—	`conf`	Causal Bandits without prior knowledge using separating sets. CLeaR Arnoud A. W. M. de Kroon, Joris M. Mooij, Danielle Belgrave
2022	—	`conf`	Local Constraint-Based Causal Discovery under Selection Bias. CLeaR Philip Versteeg, Joris M. Mooij, Cheng Zhang
2022	J	`jnl`	Local Constraint-Based Causal Discovery under Selection Bias. CoRR Philip Versteeg, Cheng Zhang, Joris M. Mooij
2022	A	`conf`	Robustness of model predictions under extension. UAI Tineke Blom, Joris M. Mooij
2021	A	`conf`	A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. UAI Philip A. Boeken, Joris M. Mooij
2021	A	`conf`	A weaker faithfulness assumption based on triple interactions. UAI Alexander Marx, Arthur Gretton, Joris M. Mooij
2021	J	`jnl`	Causality and independence in perfectly adapted dynamical systems. CoRR Tineke Blom, Joris M. Mooij
2021	J	`jnl`	Conditional independences and causal relations implied by sets of equations. J. Mach. Learn. Res. Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij
2021	J	`jnl`	Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij
2020	J	`jnl`	A Weaker Faithfulness Assumption based on Triple Interactions. CoRR Alexander Marx, Arthur Gretton, Joris M. Mooij
2020	J	`jnl`	Causal Discovery for Causal Bandits utilizing Separating Sets. CoRR Arnoud A. W. M. de Kroon, Danielle Belgrave, Joris M. Mooij
2020	J	`jnl`	Conditional Independences and Causal Relations implied by Sets of Equations. CoRR Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij
2020	J	`jnl`	Constraint-Based Causal Discovery In The Presence Of Cycles. CoRR Joris M. Mooij, Tom Claassen
2020	A	`conf`	Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. UAI Joris M. Mooij, Tom Claassen
2020	J	`jnl`	Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. Joris M. Mooij, Sara Magliacane, Tom Claassen
2020	J	`jnl`	Robustness of Model Predictions under Extension. CoRR Tineke Blom, Joris M. Mooij
2019	A	`conf`	Beyond Structural Causal Models: Causal Constraints Models. UAI Tineke Blom, Stephan Bongers, Joris M. Mooij
2019	—	`conf`	Boosting Local Causal Discovery in High-Dimensional Expression Data. BIBM Philip Versteeg, Joris M. Mooij
2019	J	`jnl`	Boosting Local Causal Discovery in High-Dimensional Expression Data. CoRR Philip Versteeg, Joris M. Mooij
2019	A	`conf`	Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. UAI Patrick Forré, Joris M. Mooij
2019	J	`jnl`	Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. CoRR Patrick Forré, Joris M. Mooij
2018	J	`jnl`	Algebraic Equivalence of Linear Structural Equation Models. CoRR Thijs van Ommen, Joris M. Mooij
2018	J	`jnl`	An Upper Bound for Random Measurement Error in Causal Discovery. CoRR Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
2018	A	`conf`	Causal Discovery in the Presence of Measurement Error. UAI Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
2018	A	`conf`	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. UAI Patrick Forré, Joris M. Mooij
2018	J	`jnl`	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. CoRR Patrick Forré, Joris M. Mooij
2018	A*	`conf`	Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. NeurIPS Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
2018	A	`conf`	From Deterministic ODEs to Dynamic Structural Causal Models. UAI Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf
2018	J	`jnl`	From Random Differential Equations to Structural Causal Models: the stochastic case. CoRR Stephan Bongers, Joris M. Mooij
2018	J	`jnl`	Generalized Strucutral Causal Models. CoRR Tineke Blom, Joris M. Mooij
2017	A	`conf`	Algebraic Equivalence Class Selection for Linear Structural Equation Models. UAI Thijs van Ommen, Joris M. Mooij
2017	A	`conf`	Causal Consistency of Structural Equation Models. UAI Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
2017	J	`jnl`	Causal Consistency of Structural Equation Models. CoRR Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
2017	—	`conf`	Causal Effect Inference with Deep Latent-Variable Models. NIPS Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Causal Effect Inference with Deep Latent-Variable Models. CoRR Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Causal Transfer Learning. CoRR Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
2017	—	`ed.`	Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016. CFA@UAI Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva
2016	—	`conf`	Ancestral Causal Inference. NIPS Sara Magliacane, Tom Claassen, Joris M. Mooij
2016	J	`jnl`	Ancestral Causal Inference. CoRR Sara Magliacane, Tom Claassen, Joris M. Mooij
2016	J	`jnl`	Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2016	J	`jnl`	From Deterministic ODEs to Dynamic Structural Causal Models. CoRR Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf
2016	J	`jnl`	Joint Causal Inference on Observational and Experimental Datasets. CoRR Sara Magliacane, Tom Claassen, Joris M. Mooij
2016	J	`jnl`	Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. CoRR Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij
2015	—	`conf`	An Empirical Study of the Simplest Causal Prediction Algorithm. ACI@UAI Jerome Cremers, Joris M. Mooij
2015	J	`jnl`	MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma
2014	J	`jnl`	Causal discovery with continuous additive noise models. J. Mach. Learn. Res. Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2014	J	`jnl`	Distinguishing cause from effect using observational data: methods and benchmarks. CoRR Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2014	J	`jnl`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2014	—	`ed.`	Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014. CI@UAI Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen
2014	—	`conf`	Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. CI@UAI Nicholas Cornia, Joris M. Mooij
2013	A	`conf`	Cyclic Causal Discovery from Continuous Equilibrium Data. UAI Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Cyclic Causal Discovery from Continuous Equilibrium Data. CoRR Joris M. Mooij, Tom Heskes
2013	J	`jnl`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case CoRR Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2013	A	`conf`	From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2013	A	`conf`	Learning Sparse Causal Models is not NP-hard. UAI Tom Claassen, Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Learning Sparse Causal Models is not NP-hard. CoRR Tom Claassen, Joris M. Mooij, Tom Heskes
2013	—	`conf`	Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2012	J	`jnl`	Identifiability of Causal Graphs using Functional Models CoRR Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2012	J	`jnl`	Identifying confounders using additive noise models CoRR Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2012	J	`jnl`	Inferring deterministic causal relations CoRR Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
2012	J	`jnl`	Information-geometric approach to inferring causal directions. Artif. Intell. Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf
2012	A*	`conf`	On causal and anticausal learning. ICML Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2012	J	`jnl`	Sufficient conditions for convergence of Loopy Belief Propagation CoRR Joris M. Mooij, Hilbert J. Kappen
2011	J	`jnl`	A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller. Neural Comput. Suzanna Martens, Joris M. Mooij, N. Jeremy Hill, Jason Farquhar, Bernhard Schölkopf
2011	—	`conf`	Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt
2011	A	`conf`	Identifiability of Causal Graphs using Functional Models. UAI Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2011	B	`conf`	Learning of causal relations. ESANN John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl
2011	—	`conf`	On Causal Discovery with Cyclic Additive Noise Models. NIPS Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
2010	—	`conf`	Distinguishing between cause and effect. NIPS Causality: Objectives and Assessment Joris M. Mooij, Dominik Janzing
2010	A	`conf`	Inferring deterministic causal relations. UAI Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
2010	—	`conf`	Probabilistic latent variable models for distinguishing between cause and effect. NIPS Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf
2010	J	`jnl`	Remote Sensing Feature Selection by Kernel Dependence Measures. IEEE Geosci. Remote. Sens. Lett. Gustavo Camps-Valls, Joris M. Mooij, Bernhard Schölkopf
2010	J	`jnl`	libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. J. Mach. Learn. Res. Joris M. Mooij
2009	A	`conf`	Identifying confounders using additive noise models. UAI Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2009	A*	`conf`	Regression by dependence minimization and its application to causal inference in additive noise models. ICML Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2008	—	`conf`	Bounds on marginal probability distributions. NIPS Joris M. Mooij, Hilbert J. Kappen
2008	—	`conf`	Nonlinear causal discovery with additive noise models. NIPS Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
2007	B	`conf`	Inference in the Promedas Medical Expert System. AIME Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn A. R. Leisink, Hilbert J. Kappen, Jan P. Neijt
2007	A	`conf`	Loop Corrected Belief Propagation. AISTATS Joris M. Mooij, Bastian Wemmenhove, Bert Kappen, Tommaso Rizzo
2007	J	`jnl`	Loop Corrections for Approximate Inference on Factor Graphs. J. Mach. Learn. Res. Joris M. Mooij, Hilbert J. Kappen
2007	J	`jnl`	Sufficient Conditions for Convergence of the Sum-Product Algorithm. IEEE Trans. Inf. Theory Joris M. Mooij, Hilbert J. Kappen
2007	J	`jnl`	Truncating the Loop Series Expansion for Belief Propagation. J. Mach. Learn. Res. Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen
2006	J	`jnl`	Loop corrections for approximate inference CoRR Joris M. Mooij, Bert Kappen
2006	J	`jnl`	Truncating the loop series expansion for Belief Propagation CoRR Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen
2005	A	`conf`	Sufficient Conditions for Convergence of Loopy Belief Propagation. UAI Joris M. Mooij, Hilbert J. Kappen
2005	J	`jnl`	Sufficient conditions for convergence of the Sum-Product Algorithm CoRR Joris M. Mooij, Hilbert J. Kappen
2004	—	`conf`	Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks. NIPS Joris M. Mooij, Hilbert J. Kappen