Mooij, Joris M.

44 publications

NeurIPS 2025 Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions Philip Boeken, Onno Zoeter, Joris M. Mooij

UAI 2024 Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence – Preface Negar Kiyavash, Joris M. Mooij

UAI 2023 Correcting for Selection Bias and Missing Response in Regression Using Privileged Information P Boeken, Noud Kroon, Mathijs Jong, Joris M. Mooij, Onno Zoeter

UAI 2023 Establishing Markov Equivalence in Cyclic Directed Graphs Tom Claassen, Joris M. Mooij

UAI 2022 Robustness of Model Predictions Under Extension Tineke Blom, Joris M. Mooij

UAI 2021 A Bayesian Nonparametric Conditional Two-Sample Test with an Application to Local Causal Discovery Philip A. Boeken, Joris M. Mooij

UAI 2021 A Weaker Faithfulness Assumption Based on Triple Interactions Alexander Marx, Arthur Gretton, Joris M. Mooij

JMLR 2021 Conditional Independences and Causal Relations Implied by Sets of Equations Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij

UAI 2020 Constraint-Based Causal Discovery Using Partial Ancestral Graphs in the Presence of Cycles Joris M. Mooij, Tom Claassen

JMLR 2020 Joint Causal Inference from Multiple Contexts Joris M. Mooij, Sara Magliacane, Tom Claassen

UAI 2019 Beyond Structural Causal Models: Causal Constraints Models Tineke Blom, Stephan Bongers, Joris M. Mooij

UAI 2019 Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias Patrick Forré, Joris M. Mooij

UAI 2018 Causal Discovery in the Presence of Measurement Error Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

UAI 2018 Constraint-Based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders Patrick Forré, Joris M. Mooij

NeurIPS 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

UAI 2018 From Deterministic ODEs to Dynamic Structural Causal Models Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf

UAI 2017 Algebraic Equivalence Class Selection for Linear Structural Equation Models Thijs van Ommen, Joris M. Mooij

UAI 2017 Causal Consistency of Structural Equation Models Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

NeurIPS 2017 Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling

NeurIPS 2016 Ancestral Causal Inference Sara Magliacane, Tom Claassen, Joris M. Mooij

JMLR 2016 Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

UAI 2016 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 Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva

UAI 2015 An Empirical Study of the Simplest Causal Prediction Algorithm Jerome Cremers, Joris M. Mooij

JMLR 2014 Causal Discovery with Continuous Additive Noise Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

UAI 2014 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 Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen

UAI 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

UAI 2013 Cyclic Causal Discovery from Continuous Equilibrium Data Joris M. Mooij, Tom Heskes

UAI 2013 From Ordinary Differential Equations to Structural Causal Models: The Deterministic Case Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

UAI 2013 Learning Sparse Causal Models Is Not NP-Hard Tom Claassen, Joris M. Mooij, Tom Heskes

ICML 2012 On Causal and Anticausal Learning Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

NeurIPS 2011 Efficient Inference in Matrix-Variate Gaussian Models with \iid Observation Noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten Borgwardt

UAI 2011 Identifiability of Causal Graphs Using Functional Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

NeurIPS 2011 On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

UAI 2010 Inferring Deterministic Causal Relations Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf

NeurIPS 2010 Probabilistic Latent Variable Models for Distinguishing Between Cause and Effect Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

MLOSS 2010 libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models Joris M. Mooij

UAI 2009 Identifying Confounders Using Additive Noise Models Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf

ICML 2009 Regression by Dependence Minimization and Its Application to Causal Inference in Additive Noise Models Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

NeurIPS 2008 Bounds on Marginal Probability Distributions Joris M. Mooij, Hilbert J. Kappen

NeurIPS 2008 Nonlinear Causal Discovery with Additive Noise Models Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf

JMLR 2007 Loop Corrections for Approximate Inference on Factor Graphs Joris M. Mooij, Hilbert J. Kappen

JMLR 2007 Truncating the Loop Series Expansion for Belief Propagation Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen

UAI 2005 Sufficient Conditions for Convergence of Loopy Belief Propagation Joris M. Mooij, Hilbert J. Kappen

NeurIPS 2004 Validity Estimates for Loopy Belief Propagation on Binary Real-World Networks Joris M. Mooij, Hilbert J. Kappen