Prof. dr. J.M. (Joris) Mooij
Faculty of Science
KDV
Visiting address
- Science Park 107
Postal address
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Postbus 94248
1090 GE Amsterdam
Contact details
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Publications
2024
Kiyavash, N., & Mooij, J. M. (2024). Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence: Preface. Proceedings of Machine Learning Research, 244, i-xi. https://proceedings.mlr.press/v244/kiyavash24a.html [details]
2023
- Blom, T., & Mooij, J. M. (2023). Causality and independence in perfectly adapted dynamical systems. Journal of Causal Inference, 11(1), Article 20210005. https://doi.org/10.1515/jci-2021-0005 [details]
2022
- Blom, T., & Mooij, J. M. (2022). Robustness of model predictions under extension. Proceedings of Machine Learning Research, 180, 213-222. https://openreview.net/forum?id=BGGevIUicl9 [details]
- Versteeg, P., Zhang, C., & Mooij, J. M. (2022). Local Constraint-Based Causal Discovery under Selection Bias. Proceedings of Machine Learning Research, 177, 840-860. https://proceedings.mlr.press/v177/versteeg22a.html [details]
- de Kroon, A. A. W. M., Belgrave, D., & Mooij, J. M. (2022). Causal Bandits without prior knowledge using separating sets. Proceedings of Machine Learning Research, 177, 407-427. https://proceedings.mlr.press/v177/kroon22a.html [details]
2021
- Blom, T., Van Diepen, M., & Mooij, J. M. (2021). Conditional independences and causal relations implied by sets of equations. Journal of Machine Learning Research, 22(178), 1-62. http://jmlr.org/papers/v22/20-863.html [details]
- Boeken, P. A., & Mooij, J. M. (2021). A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. Proceedings of Machine Learning Research, 161, 1565-1575. https://proceedings.mlr.press/v161/boeken21a.html [details]
- Bongers, S., Forré, P., Peters, J., & Mooij, J. M. (2021). Foundations of structural causal models with cycles and latent variables. The Annals of Statistics, 49(5), 2885-2915. https://doi.org/10.1214/21-AOS2064 [details]
- Marx, A., Gretton, A., & Mooij, J. M. (2021). A weaker faithfulness assumption based on triple interactions. Proceedings of Machine Learning Research, 161, 451-460. https://proceedings.mlr.press/v161/marx21a.html [details]
2020
- Mooij, J. M., & Claassen, T. (2020). Constraint-Based Causal Discovery with Partial Ancestral Graphs in the presence of Cycles. Proceedings of Machine Learning Research, 124, 1159-1168. http://proceedings.mlr.press/v124/m-mooij20a.html [details]
- Mooij, J. M., Magliacane, S., & Claassen, T. (2020). Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research, 21(99), Article 99. https://www.jmlr.org/papers/v21/ [details]
2019
- Blom, T., Bongers, S., & Mooij, J. M. (2019). Beyond Structural Causal Models: Causal Constraints Models. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 205 AUAI Press. http://auai.org/uai2019/proceedings/papers/205.pdf [details]
- Forré, P., & Mooij, J. M. (2019). Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 15 AUAI Press. http://auai.org/uai2019/proceedings/papers/15.pdf [details]
- Magliacane, S., van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., & Mooij, J. M. (2019). Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems 2018: Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10846-10856). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions [details]
2018
- Blom, T., Klimovskaia, A., Magliacane, S., & Mooij, J. M. (2018). An Upper Bound for Random Measurement Error in Causal Discovery. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 570-579). AUAI Press. http://auai.org/uai2018/proceedings/papers/208.pdf [details]
- Forré, P., & Mooij, J. M. (2018). Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 269-278). AUAI Press. http://auai.org/uai2018/proceedings/papers/117.pdf [details]
- Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf [details]
- Rubenstein, P. K., Bongers, S., Schölkopf, B., & Mooij, J. M. (2018). From Deterministic ODEs to Dynamic Structural Causal Models. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 114-123). Article 43 AUAI Press. http://auai.org/uai2018/proceedings/papers/43.pdf [details]
2017
- Magliacane, S., Claassen, T., & Mooij, J. (2017). Ancestral Causal Inference. In D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, & I. Guyon (Eds.), 30th Annual Conference on Neural Information Processing Systems 2016: Barcelona, Spain, 5-10 December 2016 (Vol. 7, pp. 4473-4481). (Advances in Neural Information Processing Systems; Vol. 29). Curran Associates. http://papers.nips.cc/paper/6266-ancestral-causal-inference [details]
- Rubenstein, P. K., Weichwald, S., Bongers, S., Mooij, J. M., Janzing, D., Grosse-Wentrup, M., & Schölkopf, B. (2017). Causal Consistency of Structural Equation Models. In G. Elidan, & K. Kersting (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Third Conference (2017) : 11-15 August 2017, Sydney, Australia Article 11 AUAI Press. http://auai.org/uai2017/proceedings/papers/11.pdf [details]
- van Ommen, T., & Mooij, J. M. (2017). Algebraic Equivalence of Linear Structural Equation Models. In G. Elidan, & K. Kersting (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Third Conference (2017) : 11-15 August 2017, Sydney, Australia Article 277 AUAI Press. http://auai.org/uai2017/proceedings/papers/277.pdf [details]
2016
- Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P. (2016). Methods for causal inference from gene perturbation experiments and validation. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7361-7368. https://doi.org/10.1073/pnas.1510493113 [details]
- Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., & Schölkopf, B. (2016). Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. Journal of Machine Learning Research, 17, Article 32. http://jmlr.org/papers/v17/14-518.html [details]
2015
- Mooij, J. M., & Cremers, J. (2015). An Empirical Study of one of the Simplest Causal Prediction Algorithms. In R. Silva, I. Shpitser, R. Evans, J. Peters, & T. Claassen (Eds.), Proceedings of the UAI 2015 Workshop on Advances in Causal Inference: co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015) : Amsterdam, The Netherlands, July 16, 2015 (pp. 30-39). Article 2 (CEUR Workshop Proceedings; Vol. 1504). CEUR-WS. http://ceur-ws.org/Vol-1504/uai2015aci_paper2.pdf [details]
- de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015). MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Computational Biology, 11(4), Article e004219. https://doi.org/10.1371/journal.pcbi.1004219 [details]
2014
- Cornia, N., & Mooij, J. M. (2014). Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. In J. M. Mooij, D. Janzing, J. Peters, T. Claassen, & A. Hyttinen (Eds.), 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 (pp. 35-42). (CEUR Workshop Proceedings; Vol. 1274). CEUR-WS. http://ceur-ws.org/Vol-1274/uai2014ci_paper7.pdf [details]
- Peters, J., Mooij, J. M., Janzing, D., & Schölkopf, B. (2014). Causal Discovery with Continuous Additive Noise Models. Journal of Machine Learning Research, 15, 2009-2053. http://jmlr.org/papers/v15/peters14a.html [details]
2013
- Claassen, T., Mooij, J. M., & Heskes, T. (2013). Learning sparse causal models is not NP-hard. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 172-181). AUAI Press. http://auai.org/uai2013/prints/papers/121.pdf [details]
- Mooij, J. M., & Heskes, T. (2013). Cyclic causal discovery from continuous equilibrium data. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 431-439). AUAI Press. http://auai.org/uai2013/prints/papers/23.pdf [details]
- Mooij, J. M., Janzing, D., & Schölkopf, B. (2013). From Ordinary Differential Equations to Structural Causal Models: the deterministic case. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 440-448). AUAI Press. http://auai.org/uai2013/prints/proceedings.pdf [details]
2022
- Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786 [details]
2014
- Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. ArXiv. http://arxiv.org/abs/1411.1557 [details]
- Mooij, J. M., Janzing, D., Peters, J., Claassen, T., & Hyttinen, A. (Eds.) (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. (CEUR Workshop Proceedings; Vol. 1274). CEUR-WS. http://ceur-ws.org/Vol-1274 [details]
2022
- Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2021
2020
- Bongers, S., Forré, P., Peters, J., Schölkopf, B., & Mooij, J. M. (2020). Foundations of Structural Causal Models with Cycles and Latent Variables. (v4 ed.) ArXiv. https://arxiv.org/abs/1611.06221v4 [details]
2017
- Forré, P., & Mooij, J. M. (2017). Markov Properties for Graphical Models with Cycles and Latent Variables. Informatics Institute, University of Amsterdam. https://arxiv.org/abs/1710.08775 [details]
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Ancillary activities
- No ancillary activities
Kiyavash, N., & Mooij, J. M. (2024). Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence: Preface. Proceedings of Machine Learning Research, 244, i-xi.