bleeding: Guideline recommendations from the international consensus group. Annals of Internal Medicine, 171(11):805-822. PMID: 31634917.
Bastani, H., Bastani, O., and Sinchaisri, W. P. (2021). Improving human decision-making with machine learning.
Berrett, T. B., Wang, Y., Barber, R. F., and Samworth, R. J. (2018). The conditional permutation test for independence while controlling for confounders.
Blatchford, O., Murray, W. R., and Blatchford, M. (2000). A risk score to predict need for treatment for upper-gastrointestinal haemorrhage. Lancet, 356(9238):1318-1321.
Camerer, C. and Johnson, E. (1991). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In Ericsson, A. and Smith, J., editors, Toward a General Theory of Expertise: Prospects and Limits. Cambridge University Press.
Candes, E., Fan, Y., Janson, L., and Lv, J. (2016). Panning for gold: Model-x knockoffs for high-dimensional controlled variable selection.
Cen, S. H. and Shah, D. (2021). Regret, stability fairness in matching markets with bandit learners.
Chatten, K., Purssell, H., Banerjee, A. K., Soteriadou, S., and Ang, Y. (2018). Glasgow blatchford score and risk stratifications in acute upper gastrointestinal bleed: can we extend this to 2 for urgent outpatient management? Clin. Med., 18(2):118-122.
Cowgill, B. (2018). Bias and productivity in humans and algorithms: Theory and evidence from resume screening.
Currie, J. and MacLeod, W. B. (2017). Diagnosing expertise: Human capital, decision making, and performance among physicians. *J. Labor Econ.*, 35(1):1-43.
Dawes, R. M. (1971). A case study of graduate admissions: Application of three principles of human decision making. Am. Psychol., 26(2):180-188.
Dawes, R. M., Faust, D., and Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243(4899):1668-1674.
Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B (Methodological), 41(1):1-31.
Edmonds, J. (1965). Paths, trees, and flowers. Canadian Journal of mathematics, 17:449-467.
Eren, O. and Mocan, N. (2018). Emotional judges and unlucky juveniles. Am. Econ. J. Appl. Econ., 10(3):171-205.
Fukumizu, K., Bach, F. R., and Jordan, M. I. (2004). Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces. J. Mach. Learn. Res., 5:73-99.
Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., and Smola, A. J. (2007). A kernel statistical test of independence. In Advances in Neural Information Processing Systems 20, pages 585-592. Curran Associates, Inc.
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., and Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis. *Psychol Assess*, 12(1):19-30.
Hardt, M., Megiddo, N., Papadimitriou, C., and Wootters, M. (2015). Strategic classification.
Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018). Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In Dy, J. and Krause, A., editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 2564-2572. PMLR.
Keswani, V., Lease, M., and Kenthapadi, K. (2021). Towards unbiased and accurate deferral to multiple experts. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.