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uva.nl

dr. J.M. (Joris) Mooij

Faculty of Science
Informatics Institute

Visiting address
  • Science Park 904
  • Room number: C2.117
Postal address
  • Postbus 94323
    1090 GH Amsterdam
Contact details
  • Publications

    2018

    • Forré, P. D., & Mooij, J. M. (2018). Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. In Conference on Uncertainty in Artificial Intelligence 2018: UAI 2018
    • 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). Red Hook, NY: Curran Associates. [details]
    • Magliacane, S., van Ommen, M., Claassen, T., Bongers, S. R., Versteeg, P. J. J. P., & Mooij, J. M. (Accepted/In press). 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.), Advances in Neural Information Processing Systems: Curran Associates, Inc. (Vol. 31, pp. 10869-10879)
    • Rubenstein, P., Bongers, S. R., Schölkopf, B., & Mooij, J. M. (2018). From Deterministic ODEs to Dynamic Structural Causal Models. In Uncertainty in Artificial Intelligence [43] AUAI Press.

    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). Red Hook, NY: Curran Associates. [details]
    • van Ommen, T., & Mooij, J. M. (2017). Algebraic Equivalence of Linear Structural Equation Models. In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI2017)
    • Rubenstein, P., Weichwald, S., Mooij, J. M., Bongers, S. R., Janzing, D., Grosse-Wentrup, M., & Schölkopf, B. (2017). Causal Consistency of Structural Equation Models. In Uncertainty in Artificial Intelligence [11] AUAI Press.

    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, [32]. [details]

    2015

    • Mooij, J. M., & Cremers, J. (2015). An Empirical Study of one of the Simplest Causal Prediction Algorithms. CEUR Workshop Proceedings, 1504, 30-39. [2]. [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]. DOI: 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. CEUR Workshop Proceedings, 1274, 35-42. [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

    • 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]
    • 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]

    2018

    • Blom, T., Klimovskaia, A., Magliacane, S., & Mooij, J. M. (2018). An Upper Bound for Random Measurement Error in Causal Discovery.

    2014

    • Mooij, J. M., Janzing, D., Peters, J., Claassen, T., & Hyttinen, A. (2014). Proceedings of the UAI 2014 Workshop: Causal Inference: Learning and Prediction: Quebec City, Canada, July 27, 2014. (CEUR Workshop Proceedings; No. 1274). Aachen: CEUR. [details]
    • Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. Ithaca, NY: arXiv.org. [details]

    2017

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