Search results
Results: 49
Number of items: 49
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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 -
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
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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
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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
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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
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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
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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
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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
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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
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