For best experience please turn on javascript and use a modern browser!
You are using a browser that is no longer supported by Microsoft. Please upgrade your browser. The site may not present itself correctly if you continue browsing.

Prof. dr. J.M. (Joris) Mooij

Faculty of Science
KDV

Visiting address
  • Science Park 107
Postal address
  • Postbus 94248
    1090 GE Amsterdam
Contact details
  • Publications

    2023

    2022

    • Blom, T., & Mooij, J. M. (2022). Robustness of model predictions under extension. Proceedings of Machine Learning Research, 180, 213-222.
    • Versteeg, P. J. J. P., Mooij, J. M., & Zhang, C. (2022). Local Constraint-Based Causal Discovery under Selection Bias. Proceedings of Machine Learning Research, 177, 840-860.
    • de Kroon, A. A. W. M., Mooij, J. M., & Belgrave, D. (2022). Causal bandits without prior knowledge using separating sets. Proceedings of Machine Learning Research, 177, 407-427.

    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. https://jmlr.org/papers/volume22/20-863/20-863.pdf
    • 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.
    • 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.

    2020

    2019

    2018

    2017

    2016

    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.), UAI2015-ACI : UAI 2015 Workshop on Advances in Causal Inference: 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). [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), [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.), UAI2014CI : UAI 2014 Workshop on Causal Inference: Learning and Prediction: 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. [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). Corvallis, Oregon: AUAI Press. [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). Corvallis, Oregon: AUAI Press. [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). Corvallis, Oregon: AUAI Press. [details]

    2022

    2014

    • Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. Ithaca, NY: arXiv.org. [details]
    • Mooij, J. M., Janzing, D., Peters, J., Claassen, T., & Hyttinen, A. (Eds.) (2014). UAI2014CI : UAI 2014 Workshop on Causal Inference: Learning and Prediction: 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

    2022

    • Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [details]
    • Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [details]

    2021

    • Blom, T. (2021). Causality and independence in systems of equations. [details]

    2017

    • Kingma, D. P. (2017). Variational inference & deep learning: A new synthesis. [details]

    2020

    2017

    • Forré, P., & Mooij, J. M. (2017). Markov Properties for Graphical Models with Cycles and Latent Variables. Amsterdam: Informatics Institute, University of Amsterdam. [details]
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Ancillary activities
    • No ancillary activities