Search results
Results: 49
Number of items: 49
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Boeken, P., de Kroon, N., de Jong, M., Mooij, J. M., & Zoeter, O. (2023). Correcting for selection bias and missing response in regression using privileged information. Proceedings of Machine Learning Research, 216, 195-205. https://proceedings.mlr.press/v216/boeken23a.html -
Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786
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Versteeg, P., Zhang, C., & Mooij, J. M. (2022). Local Constraint-Based Causal Discovery under Selection Bias. Proceedings of Machine Learning Research, 177, 840-860. https://proceedings.mlr.press/v177/versteeg22a.html
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Blom, T., & Mooij, J. M. (2022). Robustness of model predictions under extension. Proceedings of Machine Learning Research, 180, 213-222. https://openreview.net/forum?id=BGGevIUicl9
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de Kroon, A. A. W. M., Belgrave, D., & Mooij, J. M. (2022). Causal Bandits without prior knowledge using separating sets. Proceedings of Machine Learning Research, 177, 407-427. https://proceedings.mlr.press/v177/kroon22a.html
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Bongers, S., Forré, P., Peters, J., & Mooij, J. M. (2021). Foundations of structural causal models with cycles and latent variables. The Annals of Statistics, 49(5), 2885-2915. https://doi.org/10.1214/21-AOS2064
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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
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