[1] Jeroen Berrevoets, Ahmed Alaa, Zhaozhi Qian, James Jordan, Alexander ES Gimson, and Mihaela Van Der Schaar. Learning queueing policies for organ transplantation allocation using interpretable counterfactual survival analysis. In International Conference on Machine Learning, pages 792-802. PMLR, 2021.
[2] Jeroen Berrevoets, James Gordon, Ioana Bica, Alexander Gimson, and Mihaela van der Schaar. OrganITE: Optimal transplant donor organ offering using an individual treatment effect. In Advances in Neural Information Processing Systems, volume 33, pages 20037-20050. Curran Associates, Inc., 2020.
[3] Jinsung Yoon, Ahmed Alaa, Martin Cadeiras, and Mihaela Van Der Schaar. Personalized donor-recipient matching for organ transplantation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017.
[4] Jinsung Yoon, William R Zame, Amitava Banerjee, Martin Cadeiras, Ahmed M Alaa, and Mihaela van der Schaar. Personalized survival predictions via trees of predictors: An application to cardiac transplantation. *PloS one*, 13(3):e0194985, 2018.
[5] Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, and Kai Nagel. Matsim-t: Architecture and simulation times. In Multi-agent systems for traffic and transportation engineering, pages 57-78. IGI Global, 2009.
[6] Rajkumar Buyya, Rajiv Ranjan, and Rodrigo N Calheiros. Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In 2009 international conference on high performance computing & simulation, pages 1-11. IEEE, 2009.
[7] Kartik Ahuja and Mihaela Van der Schaar. Dynamic matching and allocation of tasks. ACM Transactions on Economics and Computation (TEAC), 7(4):1-27, 2019.
[8] Alex J. Chan, Ioana Bica, Alihan Hüyük, Daniel Jarrett, and Mihaela van der Schaar. The medkit-learn(ing) environment: Medical decision modelling through simulation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021. URL https://openreview.net/forum?id=Ayf90B1yESX.
[9] Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, and Mihaela van der Schaar. Causal deep learning. arXiv preprint arXiv:2303.02186, 2023.
[10] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym, 2016.
[11] Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026-5033. IEEE, 2012.
[12] Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Kuttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al. Deepmind lab. arXiv preprint arXiv:1612.03801, 2016.
[13] Charles Beattie, Thomas Köppe, Edgar A Duñez-Guzmán, and Joel Z Leibo. Deepmind lab2d. arXiv preprint arXiv:2011.07027, 2020.
[14] Agnes Debout, Yohann Foucher, Katy Trébern-Launay, Christophe Legendre, Henri Kreis, Georges Mourad, Valérie Garrigue, Emmanuel Morelon, Fanny Buron, Lionel Rostaing, et al. Each additional hour of cold ischemia time significantly increases the risk of graft failure and mortality following renal transplantation. Kidney international, 87(2):343-349, 2015.
[15] J Adam van der Vliet and Michiel C Warlé. The need to reduce cold ischemia time in kidney transplantation. Current opinion in organ transplantation, 18(2):174-178, 2013.
[16] James E Stahl, Jennifer E Kreke, Fawaz Ali Abdul Malek, Andrew J Schaefer, and Joseph Vacanti. Consequences of cold-ischemia time on primary nonfunction and patient and graft survival in liver transplantation: a meta-analysis. PloS one, 3(6):e2468, 2008.
[17] Jersey Neyman. Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes. Roczniki Nauk Rolniczych, 10:1-51, 1923.
[18] Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5):688, 1974.