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


  • Faculteit der Natuurwetenschappen, Wiskunde en Informatica
    IVI
  • Bezoekadres
    Science Park A
    Science Park 904  Amsterdam
    Kamernummer: C3.229
  • Postadres:
    Postbus  94323
    1090 GH  Amsterdam
  • J.M.Mooij@uva.nl
    T: 0205258426

2016

  • 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] [PDF]

2015

  • Mooij, J. M., & Cremers, J. (2015). An Empirical Study of one of the Simplest Causal Prediction Algorithms. CEUR Workshop Proceedings, 1504, 30-39. [details] [PDF]
  • 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] [PDF]

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] [PDF]
  • 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] [PDF]

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] [PDF]
  • 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] [PDF]
  • 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] [PDF]

2014

  • Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. Ithaca, NY: arXiv.org. [details] [PDF]
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