[39] Longford, N. T., Ely, M., Hardy, R., and Wadsworth, M. E. J. (2000). Handling missing data in diaries of alcohol consumption. Journal of the Royal Statistical Society. Series A (Statistics in Society), 163(3):381-402.
[40] Mansoor, H., Ali, S., Alam, S., Khan, M. A., Hassan, U. U., and Khan, I. (2022). Impact of missing data imputation on the fairness and accuracy of graph node classifiers. In 2022 IEEE International Conference on Big Data (Big Data), pages 5988-5997. IEEE.
[41] Marx, C., Calmon, F., and Ustun, B. (2020). Predictive multiplicity in classification. In International Conference on Machine Learning, pages 6765-6774. PMLR.
[42] Morvan, M. L., Prost, N., Josse, J., Scornet, E., and Varoquaux, G. (2020). Linear predictor on linearly-generated data with missing values: non consistency and solutions. In Chiappa, S. and Calandra, R., editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 3165-3174. PMLR.
[43] National Cancer Institute (2020). Guidance document: Missing data in seer-cahps.
[44] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
[45] Perez-Lebel, A., Varoquaux, G., Le Morvan, M., Josse, J., and Poline, J.-B. (2022). Benchmarking missing-values approaches for predictive models on health databases. *GigaScience*, 11.
[46] Polyanskiy, Y. and Wu, Y. (2019). Lecture notes on information theory. Lecture Notes for 6.441 (MIT), ECE 563 (UIUC), STAT 364 (Yale).
[47] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5):206-215.
[48] Schelter, S., He, Y., Khilnani, J., and Stoyanovich, J. (2019). Fairprep: Promoting data to a first-class citizen in studies on fairness-enhancing interventions. arXiv preprint arXiv:1911.12587.
[49] Shah, A., Shen, M., Ryu, J. J., Das, S., Sattigeri, P., Bu, Y., and Wornell, G. W. (2023). Group fairness with uncertainty in sensitive attributes. arXiv preprint arXiv:2302.08077.
[50] Sharafoddini, A., Dubin, J. A., Maslove, D. M., and Lee, J. (2019). A new insight into missing data in intensive care unit patient profiles: Observational study. *JMIR Med Inform*, 7(1):e11605.
[51] Sheridan, J. (2021). A black woman says she had to hide her race to get a fair home appraisal.
[52] Sperrin, M., Martin, G. P., Sisk, R., and Peek, N. (2020). Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology, 125:183-187.
[53] Subramonian, A., Chang, K.-W., and Sun, Y. (2022). On the discrimination risk of mean aggregation feature imputation in graphs. Advances in Neural Information Processing Systems.
[54] Tsai, J. W., Cerdeña, J. P., Goedel, W. C., Asch, W. S., Grubbs, V., Mendu, M. L., and Kaufman, J. S. (2021). Evaluating the impact and rationale of race-specific estimations of kidney function: estimations from us nhanes, 2015-2018. *EClinical Medicine*, 42:101197.
[55] Ustun, B., Liu, Y., and Parkes, D. (2019). Fairness without harm: Decoupled classifiers with preference guarantees. In International Conference on Machine Learning, pages 6373-6382. PMLR.
[56] Ustun, B. and Rudin, C. (2016). Supersparse linear integer models for optimized medical scoring systems. Machine Learning, 102:349-391.
[57] van der Heijden, G. J., T. Donders, A. R., Stijnen, T., and Moons, K. G. (2006). Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example. Journal of Clinical Epidemiology, 59(10):1102-1109.
[58] Wang, Y. and Singh, L. (2021). Analyzing the impact of missing values and selection bias on fairness. International Journal of Data Science and Analytics, 12(2):101-119.
[59] Wu, Z. and He, J. (2022). Fairness-aware model-agnostic positive and unlabeled learning. In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 1698–1708.