Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks. - Joris M. Mooij; Hilbert J. Kappen (NIPS, 2004)

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

Loop corrections for approximate inference - Joris M. Mooij; Bert Kappen (CoRR, 2006)

Inference in the Promedas Medical Expert System. - Bastian Wemmenhove; Joris M. Mooij; Wim Wiegerinck; Martijn A. R. Leisink; Hilbert J. Kappen; Jan P. Neijt (AIME, 2007)

Truncating the Loop Series Expansion for Belief Propagation. - Vicenç Gómez; Joris M. Mooij; Hilbert J. Kappen (J. Mach. Learn. Res., 2007)

Loop Corrections for Approximate Inference on Factor Graphs. - Joris M. Mooij; Hilbert J. Kappen (J. Mach. Learn. Res., 2007)

Loop Corrected Belief Propagation. - Joris M. Mooij; Bastian Wemmenhove; Bert Kappen; Tommaso Rizzo (AISTATS, 2007)

Sufficient Conditions for Convergence of the Sum-Product Algorithm. - Joris M. Mooij; Hilbert J. Kappen (IEEE Trans. Inf. Theory, 2007)

Nonlinear causal discovery with additive noise models. - Patrik O. Hoyer; Dominik Janzing; Joris M. Mooij; Jonas Peters; Bernhard Schölkopf (NIPS, 2008)

Bounds on marginal probability distributions. - Joris M. Mooij; Hilbert J. Kappen (NIPS, 2008)

Regression by dependence minimization and its application to causal inference in additive noise models. - Joris M. Mooij; Dominik Janzing; Jonas Peters; Bernhard Schölkopf (ICML, 2009)

Identifying confounders using additive noise models. - Dominik Janzing; Jonas Peters; Joris M. Mooij; Bernhard Schölkopf (UAI, 2009)

Probabilistic latent variable models for distinguishing between cause and effect. - Joris M. Mooij; Oliver Stegle; Dominik Janzing; Kun Zhang 001; Bernhard Schölkopf (NIPS, 2010)

Inferring deterministic causal relations. - Povilas Daniusis; Dominik Janzing; Joris M. Mooij; Jakob Zscheischler; Bastian Steudel; Kun Zhang 001; Bernhard Schölkopf (UAI, 2010)

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. - Joris M. Mooij (J. Mach. Learn. Res., 2010)

Distinguishing between cause and effect. - Joris M. Mooij; Dominik Janzing (NIPS Causality: Objectives and Assessment, 2010)

Remote Sensing Feature Selection by Kernel Dependence Measures. - Gustavo Camps-Valls; Joris M. Mooij; Bernhard Schölkopf (IEEE Geosci. Remote. Sens. Lett., 2010)

Learning of causal relations. - John A. Quinn; Joris M. Mooij; Tom Heskes; Michael Biehl (ESANN, 2011)

On Causal Discovery with Cyclic Additive Noise Models. - Joris M. Mooij; Dominik Janzing; Tom Heskes; Bernhard Schölkopf (NIPS, 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 (NIPS, 2011)

Identifiability of Causal Graphs using Functional Models. - Jonas Peters; Joris M. Mooij; Dominik Janzing; Bernhard Schölkopf (UAI, 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)

On causal and anticausal learning. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (ICML, 2012)

Information-geometric approach to inferring causal directions. - Dominik Janzing; Joris M. Mooij; Kun Zhang 001; Jan Lemeire; Jakob Zscheischler; Povilas Daniusis; Bastian Steudel; Bernhard Schölkopf (Artif. Intell., 2012)

Semi-supervised Learning in Causal and Anticausal Settings. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (Empirical Inference, 2013)

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

Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. - Nicholas Cornia; Joris M. Mooij (CI@UAI, 2014)

Causal discovery with continuous additive noise models. - Jonas Peters; Joris M. Mooij; Dominik Janzing; Bernhard Schölkopf (J. Mach. Learn. Res., 2014)

An Empirical Study of the Simplest Causal Prediction Algorithm. - Jerome Cremers; Joris M. Mooij (ACI@UAI, 2015)

MAGMA: Generalized Gene-Set Analysis of GWAS Data. - Christiaan A. de Leeuw; Joris M. Mooij; Tom Heskes; Danielle Posthuma (PLoS Comput. Biol., 2015)

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

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. - Stephan Bongers; Jonas Peters; Bernhard Schölkopf; Joris M. Mooij (CoRR, 2016)

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

Causal Effect Inference with Deep Latent-Variable Models. - Christos Louizos; Uri Shalit; Joris M. Mooij; David A. Sontag; Richard S. Zemel; Max Welling (NIPS, 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 (UAI, 2017)

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

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 (UAI, 2018)

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

From Random Differential Equations to Structural Causal Models: the stochastic case. - Stephan Bongers; Joris M. Mooij (CoRR, 2018)

Generalized Strucutral Causal Models. - Tineke Blom; Joris M. Mooij (CoRR, 2018)

Algebraic Equivalence of Linear Structural Equation Models. - Thijs van Ommen; Joris M. Mooij (CoRR, 2018)

An Upper Bound for Random Measurement Error in Causal Discovery. - Tineke Blom; Anna Klimovskaia; Sara Magliacane; Joris M. Mooij (CoRR, 2018)

Boosting Local Causal Discovery in High-Dimensional Expression Data. - Philip Versteeg; Joris M. Mooij (BIBM, 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, 2019)

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)

Causal Discovery for Causal Bandits utilizing Separating Sets. - Arnoud A. W. M. de Kroon; Danielle Belgrave; Joris M. Mooij (CoRR, 2020)

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

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 001; Arthur Gretton; Joris M. Mooij (UAI, 2021)

Causality and independence in perfectly adapted dynamical systems. - Tineke Blom; Joris M. Mooij (CoRR, 2021)

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. - Maximilian Ilse; Patrick Forré; Max Welling; Joris M. Mooij (CoRR, 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)

Causal Bandits without prior knowledge using separating sets. - Arnoud A. W. M. de Kroon; Joris M. Mooij; Danielle Belgrave (CLeaR, 2022)

Local Constraint-Based Causal Discovery under Selection Bias. - Philip Versteeg; Joris M. Mooij; Cheng Zhang (CLeaR, 2022)

Robustness of model predictions under extension. - Tineke Blom; Joris M. Mooij (UAI, 2022)

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 (UAI, 2023)

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

Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. - Philip A. Boeken; Onno Zoeter; Joris M. Mooij (CLeaR, 2024)

Modeling Latent Selection with Structural Causal Models. - Leihao Chen; Onno Zoeter; Joris M. Mooij (CoRR, 2024)

Robust Multi-view Co-expression Network Inference. - Teodora Pandeva; Martijs Jonker; Leendert Hamoen; Joris M. Mooij; Patrick Forré (CLeaR, 2025)

Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses. - Sourbh Bhadane; Joris M. Mooij; Philip A. Boeken; Onno Zoeter (UAI, 2025)

σ-Maximal Ancestral Graphs. - Binghua Yao; Joris M. Mooij (UAI, 2025)