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Results: 49
Number of items: 49
  • Open Access
    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
  • Open Access
    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
  • 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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. ArXiv. http://arxiv.org/abs/1411.1557
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
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