[34] Guy Horowitz and Nir Rosenfeld. Causal strategic classification: A tale of two shifts. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 13233-13253. PMLR, 2023.
[35] Xinyan Hu, Dung Ngo, Aleksandrs Slivkins, and Steven Z Wu. Incentivizing combinatorial bandit exploration. Advances in Neural Information Processing Systems, 35:37173-37183, 2022.
[36] Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, and Zhiwei Steven Wu. Bayesian exploration with heterogeneous agents. In The world wide web conference, pages 751-761, 2019.
[37] Meena Jagadeesan, Celestine Mendler-Dünnner, and Moritz Hardt. Alternative microfoundations for strategic classification. In International Conference on Machine Learning, pages 4687-4697. PMLR, 2021.
[38] Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, and Zhiwei Steven Wu. A smoothed analysis of the greedy algorithm for the linear contextual bandit problem. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 2231-2241, Red Hook, NY, USA, 2018. Curran Associates Inc.
[39] Jon Kleinberg and Manish Raghavan. How do classifiers induce agents to invest effort strategically? ACM Transactions on Economics and Computation (TEAC), 8(4):1-23, 2020.
[40] Tony Lancaster and Guido Imbens. Case-control studies with contaminated controls. Journal of Econometrics, 71(1-2):145-160, 1996.
[41] Sagi Levanon and Nir Rosenfeld. Strategic classification made practical. In International Conference on Machine Learning, pages 6243-6253. PMLR, 2021.
[42] Sagi Levanon and Nir Rosenfeld. Generalized strategic classification and the case of aligned incentives. In International Conference on Machine Learning, pages 12593-12618. PMLR, 2022.
[43] Yishay Mansour, Aleksandrs Slivkins, and Vasilis Syrgkanis. Bayesian incentive-compatible bandit exploration. In Proceedings of the Sixteenth ACM Conference on Economics and Computation, pages 565-582, 2015.
[44] Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, and Zhiwei Steven Wu. Incentivizing bandit exploration: Recommendations as instruments. In Proceedings of the 2021 International Conference on Machine Learning (ICML'21), 2021.
[45] Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, and Zhiwei Steven Wu. Incentive-aware synthetic control: Accurate counterfactual estimation via incentivized exploration. 2023. URL https://keeganharris.github.io/IE_SC.pdf.
[46] Bev O'Shea. 9 ways to build and improve your credit fast, Nov 2022. URL https://www.nerdwallet.com/article/finance raise-credit-score-fast.
[47] Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dunner, and Moritz Hardt. Performative prediction. In International Conference on Machine Learning, pages 7599-7609. PMLR, 2020.
[48] Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. The externalities of exploration and how data diversity helps exploitation. In Sébastien Bubeck, Vianney Perchet, and Philippe Rigollet, editors, Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018, volume 75 of Proceedings of Machine Learning Research, pages 1724-1738. PMLR, 2018. URL http://proceedings.mlr.press/v75/raghavan18a.html.
[49] Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. Greedy algorithm almost dominates in smoothed contextual bandits. SIAM Journal on Computing, 52(2):487-524, 2023. doi: 10.1137/19M1247115. URL https://doi.org/10.1137/19M1247115.