Search results
Results: 49
Number of items: 49
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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. http://jmlr.org/papers/v22/20-863.html -
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. https://proceedings.mlr.press/v161/boeken21a.html
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Mooij, J. M., & Claassen, T. (2020). Constraint-Based Causal Discovery with Partial Ancestral Graphs in the presence of Cycles. Proceedings of Machine Learning Research, 124, 1159-1168. http://proceedings.mlr.press/v124/m-mooij20a.html
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Mooij, J. M., Magliacane, S., & Claassen, T. (2020). Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research, 21(99), Article 99. https://www.jmlr.org/papers/v21/
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Bongers, S., Forré, P., Peters, J., Schölkopf, B., & Mooij, J. M. (2020). Foundations of Structural Causal Models with Cycles and Latent Variables. (v4 ed.) ArXiv. https://doi.org/10.48550/arXiv.1611.06221
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Blom, T., Bongers, S., & Mooij, J. M. (2019). Beyond Structural Causal Models: Causal Constraints Models. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 205 AUAI Press. http://auai.org/uai2019/proceedings/papers/205.pdf
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Forré, P., & Mooij, J. M. (2019). Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 15 AUAI Press. http://auai.org/uai2019/proceedings/papers/15.pdf
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Magliacane, S., van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., & Mooij, J. M. (2019). 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.), 32nd Conference on Neural Information Processing Systems 2018: Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10846-10856). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions
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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
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