[19] Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordan, and Mihaela van der Schaar. To impute or not to impute? missing data in treatment effect estimation. In Francisco Ruiz, Jennifer Dy, and Jan-Willem van de Meent, editors, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, pages 3568-3590. PMLR, 25-27 Apr 2023. URL https://proceedings.mlr.press/v206/berrevoets23a.html.
[20] Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin McKinney, and Mihaela van der Schaar. Disentangled counterfactual recurrent networks for treatment effect inference over time. arXiv preprint arXiv:2112.03811, 2021.
[21] Alicia Curth and Mihaela van der Schaar. Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. In International Conference on Artificial Intelligence and Statistics, pages 1810-1818. PMLR, 2021.
[22] Ioana Bica, Ahmed Alaa, and Mihaela Van Der Schaar. Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders. In International Conference on Machine Learning, pages 884-895. PMLR, 2020.
[23] C Ahn, H Amer, D Anglicheau, N Ascher, C Baan, B Bat-Ireedui, Thierry Berney, MGH Betjes, S Bichu, H Birn, et al. Global transplantation Covid report march 2020. Transplantation, 2020.
[24] Ezekiel J. Emanuel, Govind Persad, Ross Upshur, Beatzir Thome, Michael Parker, Aaron Glickman, Cathy Zhang, Connor Boyle, Maxwell Smith, and James P. Phillips. Fair allocation of scarce medical resources in the time of covid-19. New England Journal of Medicine, 382(21): 2049–2055, 2020. doi: 10.1056/NEJMsb2005114. URL https://doi.org/10.1056/ NEJMsb2005114.
[25] Marco Vergano, Guido Bertolini, Alberto Giannini, Giuseppe R. Gristina, Sergio Livigni, Giovanni Mistraletti, Luigi Riccioni, and Flavia Petrini. Clinical ethics recommendations for the allocation of intensive care treatments in exceptional, resource-limited circumstances: the Italian perspective during the COVID-19 epidemic. Critical Care, 24(165), 2020. doi: https://doi.org/10.1186/s13054-020-02891-w. URL https://doi.org/10.1186/s13054-020-02891-w.
[26] Patrick S Kamath, Russell H Wiesner, Michael Malinchoc, Walter Kremers, Terry M Therneau, Catherine L Kosberg, Gennaro D'Amico, E Rolland Dickson, and W Ray Kim. A model to predict survival in patients with end-stage liver disease. Hepatology, 33(2):464-470, 2001.
[27] Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, pages 56 - 61, 2010. doi: 10.25080/Majora-92bf1922-00a.
[28] The pandas development team. pandas-dev/pandas: Pandas, February 2020. URL https://doi.org/10.5281/zenodo.3509134.
[29] Katie L Connor, Eoin D O'Sullivan, Lorna P Marson, Stephen J Wigmore, and Ewen M Harrison. The future role of machine learning in clinical transplantation. Transplantation, 105 (4):723-735, 2021.
[30] Dennis Medved, Pierre Nugues, and Johan Nilsson. Simulating the outcome of heart allocation policies using deep neural networks. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 6141-6144. IEEE, 2018.
[31] Manuel Dorado-Moreno, María Pérez-Ortiz, Pedro A Gutierrez, Rubén Ciria, Javier Briceno, and César Hervás-Martínez. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artificial Intelligence in Medicine, 77: 1-11, 2017.
[32] Dennis Medved, Mattias Ohlsson, Peter Hoglund, Bodil Andersson, Pierre Nugues, and Johan Nilsson. Improving prediction of heart transplantation outcome using deep learning techniques. Scientific reports, 8(1):1-9, 2018.
[33] Kyung Don Yoo, Junhyug Noh, Hajeong Lee, Dong Ki Kim, Chun Soo Lim, Young Hoon Kim, Jung Pyo Lee, Gunhee Kim, and Yon Su Kim. A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Scientific reports, 7(1):1-12, 2017.