[1] Ahmed M Alaa and Mihaela Van Der Schaar. Bayesian inference of individualized treatment effects using multi-task gaussian processes. Advances in neural information processing systems, 30, 2017.
[2] Tim Althoff, Hamed Nilforoshan, Jenna Hua, and Jure Leskovec. Large-scale diet tracking data reveal disparate associations between food environment and diet. Nature communications, 13(1):267, 2022.
[3] Susan Athey and Guido Imbens. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27):7353-7360, 2016.
[4] Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier. Learning a synaptic learning rule. CiteSeer, 1990.
[5] Ioana Bica, Ahmed M Alaa, Craig Lambert, and Mihaela Van Der Schaar. From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges. Clinical Pharmacology & Therapeutics, 109(1):87-100, 2021.
[6] Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. Concentration inequalities: A nonasymptotic theory of independence. Oxford university press, 2013.
[7] Olivier Bousquet. A pennett concentration inequality and its application to suprema of empirical processes. Comptes Rendus Mathematique, 334(6):495-500, 2002.
[8] Payal Chandak, Kexin Huang, and Marinka Zitnik. Building a knowledge graph to enable precision medicine. bioRxiv, 2022.
[9] Hong-Bin Chen, Sinho Chewi, and Jonathan Niles-Weed. Dimension-free log-sobolev inequalities for mixture distributions. Journal of Functional Analysis, 281(11):109236, 2021.
[10] Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. Double/debiased machine learning for treatment and structural parameters, 2018.
[11] Victor Chernozhukov, Mert Demirer, Esther Duflo, and Ivan Fernandez-Val. Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in india. Technical report, National Bureau of Economic Research, 2018.
[12] Sam Corbett-Davies, J Gaebler, Hamed Nilforoshan, Ravi Shroff, and Sharad Goel. The measure and mismeasure of fairness. J. Mach. Learn. Res, 2023.
[13] Richard K Crump, V Joseph Hotz, Guido W Imbens, and Oscar A Mitnik. Nonparametric tests for treatment effect heterogeneity. The Review of Economics and Statistics, 90(3):389-405, 2008.
[14] Alicia Curth, David Svensson, Jim Weatherall, and Mihaela van der Schaar. Really doing great at estimating cate? a critical look at ml benchmarking practices in treatment effect estimation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
[15] Alicia Curth and Mihaela van der Schaar. Doing great at estimating cate? on the neglected assumptions in benchmark comparisons of treatment effect estimators. arXiv preprint arXiv:2107.13346, 2021.
[16] 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.
[17] Alicia Curth and Mihaela van der Schaar. On inductive biases for heterogeneous treatment effect estimation. Advances in Neural Information Processing Systems, 34:15883-15894, 2021.
[18] Gerald DeJong and Raymond Mooney. Explanation-based learning: An alternative view. Machine learning, 1986.
[19] Qiaonan Duan, Corey Flynn, Mario Niepel, Marc Hafner, Jeremy L Muhlich, Nicolas F Fernandez, Andrew D Rouillard, Christopher M Tan, Edward Y Chen, Todd R Golub, et al. Lincs canvas browser: interactive web app to query, browse and interrogate lincs 11000 gene expression signatures. Nucleic acids research, 42(W1):W449-W460, 2014.