[37] Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree. Regularisation of neural networks by enforcing lipschitz continuity. Machine Learning, 110:393-416, 2021.
[38] Arash Nasr-Esfahany and Emre Kiciman. Counterfactual (non-)identifiability of learned structural causal models. arXiv preprint arXiv:2301.09031, 2023.
[39] Scott Sussex, Caroline Uhler, and Andreas Krause. Near-optimal multi-perturbation experimental design for causal structure learning. Advances in Neural Information Processing Systems, 34:777-788, 2021.
[40] Ioana Bica, Daniel Jarrett, and Mihaela van der Schaar. Invariant causal imitation learning for generalizable policies. Advances in Neural Information Processing Systems, 34:3952-3964, 2021.
[41] Chris Cundy, Aditya Grover, and Stefano Ermon. Bcd nets: Scalable variational approaches for bayesian causal discovery. Advances in Neural Information Processing Systems, 34:7095-7110, 2021.
[42] Virginia Aglietti, Neil Dhir, Javier González, and Theodoros Damoulas. Dynamic causal bayesian optimization. Advances in Neural Information Processing Systems, 34:10549-10560, 2021.
[43] Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, and Le Song. Multi-task learning of order-consistent causal graphs. Advances in Neural Information Processing Systems, 34:11083-11095, 2021.
[44] Guy Tennenholtz, Uri Shalit, and Shie Mannor. Off-policy evaluation in partially observable environments. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 10276-10283, 2020.
[45] Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, and Emma Brunskill. Off-policy policy evaluation for sequential decisions under unobserved confounding. Advances in Neural Information Processing Systems, 33:18819-18831, 2020.
[46] Lingxiao Wang, Zhuoran Yang, and Zhaoran Wang. Provably efficient causal reinforcement learning with confounded observational data. Advances in Neural Information Processing Systems, 34:21164-21175, 2021.
[47] Junzhe Zhang and Elias Bareinboim. Near-optimal reinforcement learning in dynamic treatment regimes. Advances in Neural Information Processing Systems, 32, 2019.
[48] Junzhe Zhang and Elias Bareinboim. Designing optimal dynamic treatment regimes: A causal reinforcement learning approach. In International Conference on Machine Learning, pages 11012-11022. PMLR, 2020.
[49] Ilya Shpitser and Eli Sherman. Identification of personalized effects associated with causal pathways. In Uncertainty in artificial intelligence: proceedings of the... conference. Conference on Uncertainty in Artificial Intelligence, volume 2018. NIH Public Access, 2018.
[50] Juan Correa, Sanghack Lee, and Elias Bareinboim. Nested counterfactual identification from arbitrary surrogate experiments. Advances in Neural Information Processing Systems, 34:6856-6867, 2021.
[51] Junzhe Zhang, Jin Tian, and Elias Bareinboim. Partial counterfactual identification from observational and experimental data. In International Conference on Machine Learning, pages 26548-26558. PMLR, 2022.
[52] Stuart Russel, Peter Norvig, et al. Artificial intelligence: a modern approach, volume 256. Pearson Education Limited London, 2013.
[53] Judea Pearl. Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley Longman Publishing Co., Inc., 1984.