[75] Nicholas P Tatonetti, Patrick P Ye, Roxana Daneshjou, and Russ B Altman. Data-driven prediction of drug effects and interactions. Science translational medicine, 4(125):125ra31-125ra31, 2012.
[76] Sebastian Thrun and Lorien Pratt. Learning to learn. Springer Science & Business Media, 2012.
[77] Nilesh Tripuraneni, Dhruv Madeka, Dean Foster, Dominique Perrault-Joncas, and Michael I Jordan. Meta-analysis of randomized experiments with applications to heavy-tailed response data. arXiv preprint arXiv:2112.07602, 2021.
[78] Vladimir Vapnik. Principles of risk minimization for learning theory. Advances in neural information processing systems, 4, 1991.
[79] Victor Veitch, Dhanya Sridhar, and David Blei. Adapting text embeddings for causal inference. In Conference on Uncertainty in Artificial Intelligence, pages 919–928. PMLR, 2020.
[80] Stefan Wager. Stats 361: Causal inference, 2020.
[81] Stefan Wager and Susan Athey. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523):1228-1242, 2018.
[82] Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao. A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1-37, 2019.
[83] Yixin Wang and David M Blei. The blessings of multiple causes. Journal of the American Statistical Association, 114(528):1574-1596, 2019.
[84] Galen Weld, Peter West, Maria Glenski, David Arbour, Ryan A Rossi, and Tim Althoff. Adjusting for confounders with text: Challenges and an empirical evaluation framework for causal inference. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 1109–1120, 2022.
[85] Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning-the good, the bad and the ugly. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4582-4591, 2017.
[86] Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, and Stefan Wager. Evaluating treatment prioritization rules via rank-weighted average treatment effects. arXiv preprint arXiv:2111.07966, 2021.
[87] Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. QA-GNN: Reasoning with language models and knowledge graphs for question answering. In North American Chapter of the Association for Computational Linguistics (NAACL), 2021.
[88] Shing-Tung Yau. Isoperimetric constants and the first eigenvalue of a compact riemannian manifold. In Annales Scientifiques de l'École Normale Supérieure, volume 8, pages 487-507, 1975.
[89] Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. Ganite: Estimation of individualized treatment effects using generative adversarial nets. In International Conference on Learning Representations, 2018.
[90] Long Yu, Zhicong Luo, Huanyong Liu, Deng Lin, Hongzhu Li, and Yafeng Deng. Triplere: Knowledge graph embeddings via tripled relation vectors. arXiv preprint arXiv:2209.08271, 2022.
[91] Yao Zhang, Alexis Bellot, and Mihaela Schaar. Learning overlapping representations for the estimation of individualized treatment effects. In International Conference on Artificial Intelligence and Statistics, pages 1005–1014. PMLR, 2020.
[92] Marinka Zitnik, Monica Agrawal, and Jure Leskovec. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13):i457-i466, 2018.