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ORCIDJoris M. Mooij
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2020 – today
- 2024
[c42]Philip A. Boeken, Onno Zoeter, Joris M. Mooij:
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. CLeaR 2024: 551-569- [i38]Leihao Chen, Onno Zoeter, Joris M. Mooij:
Modeling Latent Selection with Structural Causal Models. CoRR abs/2401.06925 (2024) - [i37]Philip A. Boeken, Onno Zoeter, Joris M. Mooij:
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. CoRR abs/2403.00886 (2024) - [i36]Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré:
Robust Multi-view Co-expression Network Inference. CoRR abs/2409.19991 (2024) - 2023
- [c41]Philip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter:
Correcting for selection bias and missing response in regression using privileged information. UAI 2023: 195-205 - [c40]Tom Claassen, Joris M. Mooij:
Establishing Markov equivalence in cyclic directed graphs. UAI 2023: 433-442 - [i35]Tom Claassen, Joris M. Mooij:
Establishing Markov Equivalence in Cyclic Directed Graphs. CoRR abs/2309.03092 (2023) - 2022
- [c39]Arnoud A. W. M. de Kroon, Joris M. Mooij, Danielle Belgrave:
Causal Bandits without prior knowledge using separating sets. CLeaR 2022: 407-427 - [c38]Philip Versteeg, Joris M. Mooij, Cheng Zhang:
Local Constraint-Based Causal Discovery under Selection Bias. CLeaR 2022: 840-860 - [c37]Tineke Blom, Joris M. Mooij:
Robustness of model predictions under extension. UAI 2022: 213-222 - [i34]Philip Versteeg, Cheng Zhang, Joris M. Mooij:
Local Constraint-Based Causal Discovery under Selection Bias. CoRR abs/2203.01848 (2022) - 2021
- [j12]Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij:
Conditional independences and causal relations implied by sets of equations. J. Mach. Learn. Res. 22: 178:1-178:62 (2021) - [c36]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A weaker faithfulness assumption based on triple interactions. UAI 2021: 451-460 - [c35]Philip A. Boeken, Joris M. Mooij:
A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. UAI 2021: 1565-1575 - [i33]Tineke Blom, Joris M. Mooij:
Causality and independence in perfectly adapted dynamical systems. CoRR abs/2101.11885 (2021) - [i32]Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij:
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR abs/2103.04786 (2021) - 2020
- [j11]Joris M. Mooij, Sara Magliacane, Tom Claassen:
Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. 21: 99:1-99:108 (2020) - [c34]Joris M. Mooij, Tom Claassen:
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. UAI 2020: 1159-1168 - [i31]Joris M. Mooij, Tom Claassen:
Constraint-Based Causal Discovery In The Presence Of Cycles. CoRR abs/2005.00610 (2020) - [i30]Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij:
Conditional Independences and Causal Relations implied by Sets of Equations. CoRR abs/2007.07183 (2020) - [i29]Arnoud A. W. M. de Kroon, Danielle Belgrave, Joris M. Mooij:
Causal Discovery for Causal Bandits utilizing Separating Sets. CoRR abs/2009.07916 (2020) - [i28]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A Weaker Faithfulness Assumption based on Triple Interactions. CoRR abs/2010.14265 (2020) - [i27]Tineke Blom, Joris M. Mooij:
Robustness of Model Predictions under Extension. CoRR abs/2012.04723 (2020)
2010 – 2019
- 2019
- [c33]Philip Versteeg, Joris M. Mooij:
Boosting Local Causal Discovery in High-Dimensional Expression Data. BIBM 2019: 2599-2604 - [c32]Patrick Forré, Joris M. Mooij:
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. UAI 2019: 71-80 - [c31]Tineke Blom, Stephan Bongers, Joris M. Mooij:
Beyond Structural Causal Models: Causal Constraints Models. UAI 2019: 585-594 - [i26]Patrick Forré, Joris M. Mooij:
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. CoRR abs/1901.00433 (2019) - [i25]Philip Versteeg, Joris M. Mooij:
Boosting Local Causal Discovery in High-Dimensional Expression Data. CoRR abs/1910.02505 (2019) - 2018
- [c30]Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij:
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. NeurIPS 2018: 10869-10879 - [c29]Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf:
From Deterministic ODEs to Dynamic Structural Causal Models. UAI 2018: 114-123 - [c28]Patrick Forré, Joris M. Mooij:
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. UAI 2018: 269-278 - [c27]Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij:
Causal Discovery in the Presence of Measurement Error. UAI 2018: 570-579 - [i24]Stephan Bongers, Joris M. Mooij:
From Random Differential Equations to Structural Causal Models: the stochastic case. CoRR abs/1803.08784 (2018) - [i23]Tineke Blom, Joris M. Mooij:
Generalized Strucutral Causal Models. CoRR abs/1805.06539 (2018) - [i22]Patrick Forré, Joris M. Mooij:
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. CoRR abs/1807.03024 (2018) - [i21]Thijs van Ommen, Joris M. Mooij:
Algebraic Equivalence of Linear Structural Equation Models. CoRR abs/1807.03527 (2018) - [i20]Tineke Blom, Anna Klimovskaia
, Sara Magliacane, Joris M. Mooij:
An Upper Bound for Random Measurement Error in Causal Discovery. CoRR abs/1810.07973 (2018) - 2017
- [c26]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017: 6446-6456 - [c25]Thijs van Ommen, Joris M. Mooij:
Algebraic Equivalence Class Selection for Linear Structural Equation Models. UAI 2017 - [c24]Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models. UAI 2017 - [e2]Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva:
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. CEUR Workshop Proceedings 1792, CEUR-WS.org 2017 [contents] - [i19]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - [i18]Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models. CoRR abs/1707.00819 (2017) - [i17]Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij:
Causal Transfer Learning. CoRR abs/1707.06422 (2017) - 2016
- [j10]Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. 17: 32:1-32:102 (2016) - [c23]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Ancestral Causal Inference. NIPS 2016: 4466-4474 - [i16]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Ancestral Causal Inference. CoRR abs/1606.07035 (2016) - [i15]Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf:
From Deterministic ODEs to Dynamic Structural Causal Models. CoRR abs/1608.08028 (2016) - [i14]Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij:
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. CoRR abs/1611.06221 (2016) - [i13]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Joint Causal Inference on Observational and Experimental Datasets. CoRR abs/1611.10351 (2016) - 2015
- [j9]Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma:
MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. 11(4) (2015) - [c22]Jerome Cremers, Joris M. Mooij:
An Empirical Study of the Simplest Causal Prediction Algorithm. ACI@UAI 2015: 30-39 - 2014
- [j8]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15(1): 2009-2053 (2014) - [c21]Nicholas Cornia, Joris M. Mooij:
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. CI@UAI 2014: 35-42 - [e1]Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen:
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. CEUR Workshop Proceedings 1274, CEUR-WS.org 2014 [contents] - [i12]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1408.2063 (2014) - [i11]Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing cause from effect using observational data: methods and benchmarks. CoRR abs/1412.3773 (2014) - 2013
- [c20]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference 2013: 129-141 - [c19]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. UAI 2013 - [c18]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. UAI 2013 - [c17]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI 2013 - [i10]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1304.7920 (2013) - [i9]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. CoRR abs/1309.6824 (2013) - [i8]Joris M. Mooij, Tom Heskes:
Cyclic Causal Discovery from Continuous Equilibrium Data. CoRR abs/1309.6849 (2013) - 2012
- [j7]Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf:
Information-geometric approach to inferring causal directions. Artif. Intell. 182-183: 1-31 (2012) - [c16]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. ICML 2012 - [i7]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. CoRR abs/1202.3757 (2012) - [i6]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. CoRR abs/1203.3475 (2012) - [i5]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. CoRR abs/1205.2640 (2012) - [i4]Joris M. Mooij, Hilbert J. Kappen:
Sufficient conditions for convergence of Loopy Belief Propagation. CoRR abs/1207.1405 (2012) - 2011
- [j6]Suzanna Martens, Joris M. Mooij, N. Jeremy Hill, Jason Farquhar, Bernhard Schölkopf:
A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller. Neural Comput. 23(1): 160-182 (2011) - [c15]John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl:
Learning of causal relations. ESANN 2011 - [c14]Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt:
Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS 2011: 630-638 - [c13]Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf:
On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647 - [c12]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. UAI 2011: 589-598 - 2010
- [j5]Joris M. Mooij:
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. J. Mach. Learn. Res. 11: 2169-2173 (2010) - [j4]Gustavo Camps-Valls, Joris M. Mooij, Bernhard Schölkopf:
Remote Sensing Feature Selection by Kernel Dependence Measures. IEEE Geosci. Remote. Sens. Lett. 7(3): 587-591 (2010) - [c11]Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf:
Probabilistic latent variable models for distinguishing between cause and effect. NIPS 2010: 1687-1695 - [c10]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. UAI 2010: 143-150 - [c9]Joris M. Mooij, Dominik Janzing:
Distinguishing between cause and effect. NIPS Causality: Objectives and Assessment 2010: 147-156
2000 – 2009
- 2009
- [c8]Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Regression by dependence minimization and its application to causal inference in additive noise models. ICML 2009: 745-752 - [c7]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. UAI 2009: 249-257 - 2008
- [c6]Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf:
Nonlinear causal discovery with additive noise models. NIPS 2008: 689-696 - [c5]Joris M. Mooij, Hilbert J. Kappen:
Bounds on marginal probability distributions. NIPS 2008: 1105-1112 - 2007
- [j3]Joris M. Mooij, Hilbert J. Kappen:
Loop Corrections for Approximate Inference on Factor Graphs. J. Mach. Learn. Res. 8: 1113-1143 (2007) - [j2]Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen:
Truncating the Loop Series Expansion for Belief Propagation. J. Mach. Learn. Res. 8: 1987-2016 (2007) - [j1]Joris M. Mooij, Hilbert J. Kappen:
Sufficient Conditions for Convergence of the Sum-Product Algorithm. IEEE Trans. Inf. Theory 53(12): 4422-4437 (2007) - [c4]Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn A. R. Leisink, Hilbert J. Kappen, Jan P. Neijt:
Inference in the Promedas Medical Expert System. AIME 2007: 456-460 - [c3]Joris M. Mooij, Bastian Wemmenhove, Bert Kappen, Tommaso Rizzo:
Loop Corrected Belief Propagation. AISTATS 2007: 331-338 - 2006
- [i3]Joris M. Mooij, Bert Kappen:
Loop corrections for approximate inference. CoRR abs/cs/0612030 (2006) - [i2]Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen:
Truncating the loop series expansion for Belief Propagation. CoRR abs/cs/0612109 (2006) - 2005
- [c2]Joris M. Mooij, Hilbert J. Kappen:
Sufficient Conditions for Convergence of Loopy Belief Propagation. UAI 2005: 396-403 - [i1]Joris M. Mooij, Hilbert J. Kappen:
Sufficient conditions for convergence of the Sum-Product Algorithm. CoRR abs/cs/0504030 (2005) - 2004
- [c1]Joris M. Mooij, Hilbert J. Kappen:
Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks. NIPS 2004: 945-952
Coauthor Index
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