| 2025 | UAI | Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses. | Sourbh Bhadane, Joris M. Mooij, Philip A. Boeken, Onno Zoeter |
| 2025 | UAI | σ-Maximal Ancestral Graphs. | Binghua Yao, Joris M. Mooij |
| 2023 | UAI | 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 |
| 2023 | UAI | Establishing Markov equivalence in cyclic directed graphs. | Tom Claassen, Joris M. Mooij |
| 2022 | UAI | Robustness of model predictions under extension. | Tineke Blom, Joris M. Mooij |
| 2021 | UAI | A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. | Philip A. Boeken, Joris M. Mooij |
| 2021 | UAI | A weaker faithfulness assumption based on triple interactions. | Alexander Marx, Arthur Gretton, Joris M. Mooij |
| 2020 | UAI | Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. | Joris M. Mooij, Tom Claassen |
| 2019 | UAI | Beyond Structural Causal Models: Causal Constraints Models. | Tineke Blom, Stephan Bongers, Joris M. Mooij |
| 2019 | UAI | Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. | Patrick Forr, Joris M. Mooij |
| 2018 | UAI | Causal Discovery in the Presence of Measurement Error. | Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij |
| 2018 | UAI | Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. | Patrick Forr, Joris M. Mooij |
| 2018 | UAI | From Deterministic ODEs to Dynamic Structural Causal Models. | Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schlkopf |
| 2017 | UAI | Algebraic Equivalence Class Selection for Linear Structural Equation Models. | Thijs van Ommen, Joris M. Mooij |
| 2017 | UAI | Causal Consistency of Structural Equation Models. | Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schlkopf |
| 2015 | UAI | An Empirical Study of the Simplest Causal Prediction Algorithm. | Jerome Cremers, Joris M. Mooij |
| 2014 | UAI | Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. | Nicholas Cornia, Joris M. Mooij |
| 2013 | UAI | Learning Sparse Causal Models is not NP-hard. | Tom Claassen, Joris M. Mooij, Tom Heskes |
| 2013 | UAI | Cyclic Causal Discovery from Continuous Equilibrium Data. | Joris M. Mooij, Tom Heskes |
| 2013 | UAI | From Ordinary Differential Equations to Structural Causal Models: the deterministic case. | Joris M. Mooij, Dominik Janzing, Bernhard Schlkopf |
| 2012 | ICML | On causal and anticausal learning. | Bernhard Schlkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij |
| 2011 | ESANN | Learning of causal relations. | John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl |
| 2011 | UAI | Identifiability of Causal Graphs using Functional Models. | Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schlkopf |
| 2010 | UAI | Inferring deterministic causal relations. | Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schlkopf |
| 2009 | ICML | Regression by dependence minimization and its application to causal inference in additive noise models. | Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schlkopf |
| 2009 | UAI | Identifying confounders using additive noise models. | Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schlkopf |
| 2007 | AIME | Inference in the Promedas Medical Expert System. | Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn A. R. Leisink, Hilbert J. Kappen, Jan P. Neijt |
| 2005 | UAI | Sufficient Conditions for Convergence of Loopy Belief Propagation. | Joris M. Mooij, Hilbert J. Kappen |