[36] Edward H Kennedy. Optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497, 2020.
[37] Edward H Kennedy. Towards optimal doubly robust estimation of heterogeneous causal effects (2020). URL https://arxiv.org/abs, 2020.
[38] Jitender Mohan Khunger, S Arulselvi, Uma Sharma, Sunil Ranga, and VH Talib. Pancytopenia—a clinico haematological study of 200 cases. Indian journal of pathology & microbiology, 45(3):375-379, 2002.
[39] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning (ICML), 2021.
[40] Andrei V Konstantinov, Stanislav R Kirpichenko, and Lev V Utkin. Heterogeneous treatment effect with trained kernels of the nadaraya-watson regression. arXiv preprint arXiv:2207.09139, 2022.
[41] N Kostantinos. Gaussian mixtures and their applications to signal processing. Advanced signal processing handbook: theory and implementation for radar, sonar, and medical imaging real time systems, pages 3-1, 2000.
[42] Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork. The sider database of drugs and side effects. *Nucleic acids research*, 44(D1):D1075–D1079, 2016.
[43] R Kumar, SP Kalra, H Kumar, AC Anand, and H Madan. Pancytopenia-a six year study. The Journal of the Association of Physicians of India, 49:1078-1081, 2001.
[44] Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences, 116(10):4156-4165, 2019.
[45] Nikolay Kuznetsov and Alexander Nazarov. Sharp constants in the poincaré, steklov and related inequalities (a survey). Mathematika, 61(2):328-344, 2015.
[46] Greg Landrum et al. Rdkit: Open-source cheminformatics. 2006.
[47] Michel Ledoux. Concentration of measure and logarithmic sobolev inequalities. In *Seminaire de probabilités XXXIII*, pages 120–216. Springer, 1999.
[48] Hongzhu Li, Xiangrui Gao, and Yafeng Deng. Stargraph: A coarse-to-fine representation method for large-scale knowledge graph, 2022.
[49] Michelle M Li, Kexin Huang, and Marinka Zitnik. Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering, pages 1-17, 2022.
[50] Jing Ma, Ruocheng Guo, Aidong Zhang, and Jundong Li. Multi-cause effect estimation with disentangled confounder representation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021.
[51] Ron Meir and Tong Zhang. Generalization error bounds for bayesian mixture algorithms. Journal of Machine Learning Research, 4(Oct):839-860, 2003.
[52] Stephen L Morgan and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015.
[53] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[54] Alex Nichol and John Schulman. Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2(3):4, 2018.
[55] Xinkun Nie and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2):299-319, 2021.
[56] Frank Nielsen and Richard Nock. On the chi square and higher-order chi distances for approximating f-divergences. IEEE Signal Processing Letters, 21(1):10-13, 2013.
[57] Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, and Sharad Goel. Causal conceptions of fairness and their consequences. In International Conference on Machine Learning, pages 16848-16887. PMLR, 2022.