Treatment Allocation with Strategic Agents

Published Online:https://doi.org/10.1287/mnsc.2022.01629

References

  • Adjaho C, Christensen T (2023) Externally valid treatment choice. Preprint, submitted July 2, https://arxiv.org/abs/2205.05561.Google Scholar
  • Ahmadi S, Beyhaghi H, Blum A, Naggita K (2021) The strategic perceptron. Proc. 22nd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 6–25.Google Scholar
  • Akerlof GA (1970) The market for “lemons”: Quality uncertainty and the market mechanism. Quart. J. Econom. 84(3):488–500.CrossrefGoogle Scholar
  • Ascarza E (2018) Retention futility: Targeting high-risk customers might be ineffective. J. Marketing Res. 55(1):80–98.CrossrefGoogle Scholar
  • Athey S (2017) Beyond prediction: Using big data for policy problems. Science 355(6324):483–485.CrossrefGoogle Scholar
  • Athey S, Imbens GW (2017) The econometrics of randomized experiments. Banerjee A, Duflo E, eds. Handbook of Economic Field Experiments, vol. 1 (North-Holland, Amsterdam), 73–140.CrossrefGoogle Scholar
  • Athey S, Wager S (2021) Policy learning with observational data. Econometrica 89(1):133–161.CrossrefGoogle Scholar
  • Auer P (2002) Using confidence bounds for exploitation-exploration trade-offs. J. Machine Learn. Res. 3:397–422.Google Scholar
  • Baird S, Bohren JA, McIntosh C, Özler B (2018) Optimal design of experiments in the presence of interference. Rev. Econom. Statist. 100(5):844–860.CrossrefGoogle Scholar
  • Bajari P, Burdick B, Imbens GW, Masoero L, McQueen J, Richardson T, Rosen IM (2021) Multiple randomization designs. Preprint, submitted December 27, https://arxiv.org/abs/2112.13495.Google Scholar
  • Ball I (2023) Scoring strategic agents. Preprint, submitted October 10, https://arxiv.org/abs/1909.01888.Google Scholar
  • Banerjee AV (1997) A theory of misgovernance. Quart. J. Econom. 112(4):1289–1332.CrossrefGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bhattacharya D, Dupas P (2012) Inferring welfare maximizing treatment assignment under budget constraints. J. Econometrics 167(1):168–196.CrossrefGoogle Scholar
  • Björkegren D, Grissen D (2020) Behavior revealed in mobile phone usage predicts credit repayment. World Bank Econom. Rev. 34(3):618–634.CrossrefGoogle Scholar
  • Björkegren D, Blumenstock JE, Knight S (2020) Manipulation-proof machine learning. Preprint, submitted April 8, https://arxiv.org/abs/2004.03865.Google Scholar
  • Braverman M, Garg S (2020) The role of randomness and noise in strategic classification. Roth A, ed. First Sympos. Foundations Responsible Comput. (Dagstuhl Publishing, Germany), 9:1–9:20.Google Scholar
  • Brown G, Hod S, Kalemaj I (2022) Performative prediction in a stateful world. Camps-Valls G, Ruiz FJR, Valera I, eds. Proc. 25th Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 6045–6061.Google Scholar
  • Chen B, Frazier P, Kempe D (2018) Incentivizing exploration by heterogeneous users. Bubeck S, Perchet V, Rigollet P, eds. Proc. 31st Internat. Conf. Machine Learn. (PMLR, New York), 798–818.Google Scholar
  • Chen Y, Liu Y, Podimata C (2020) Learning strategy-aware linear classifiers. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Adv. Neural Inform. Processing Systems, vol. 33 (Curran Associates, Inc., Red Hook, NY), 15265–15276.Google Scholar
  • Cornfield J (1978) Randomization by group: A formal analysis. Amer. J. Epidemiology 108(2):100–102.CrossrefGoogle Scholar
  • Dani V, Hayes TP, Kakade SM (2008) Stochastic linear optimization under bandit feedback. 21st Annual Conf. Learn. Theory, 355–366.Google Scholar
  • Dong J, Roth A, Schutzman Z, Waggoner B, Wu ZS (2018) Strategic classification from revealed preferences. Proc. 2018 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 55–70.Google Scholar
  • Duchi JC, Namkoong H (2021) Learning models with uniform performance via distributionally robust optimization. Ann. Statist. 49(3):1378–1406.CrossrefGoogle Scholar
  • Flaxman AD, Kalai AT, McMahan HB (2005) Online convex optimization in the bandit setting: Gradient descent without a gradient. Proc. 16th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 385–394.Google Scholar
  • Frankel A, Kartik N (2022) Improving information from manipulable data. J. Eur. Econom. Assoc. 20(1):79–115.CrossrefGoogle Scholar
  • Frazier P, Kempe D, Kleinberg J, Kleinberg R (2014) Incentivizing exploration. Proc. 15th ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 5–22.Google Scholar
  • Garnett R (2023) Bayesian Optimization (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Grossman SJ, Hart OD (1981) Implicit contracts, moral hazard, and unemployment. Amer. Econom. Rev. 71(2):301–307.Google Scholar
  • Haghtalab N, Immorlica N, Lucier B, Wang JZ (2021) Maximizing welfare with incentive-aware evaluation mechanisms. Bessiere C, ed. Proc. 29th Internat. Joint Conf. Artificial Intelligence (Yokohama, Japan), 160–166.Google Scholar
  • Hamburg MA, Collins FS (2010) The path to personalized medicine. New England J. Medicine 363(4):301–304.CrossrefGoogle Scholar
  • Hardt M, Megiddo N, Papadimitriou C, Wootters M (2016) Strategic classification. Proc. 2016 ACM Conf. Innovations Theoretical Comput. Sci. (Association for Computing Machinery, New York), 111–122.Google Scholar
  • Harshaw C, Sävje F, Eisenstat D, Mirrokni V, Pouget-Abadie J (2022) Design and analysis of bipartite experiments under a linear exposure-response model. Proc. 23rd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York).Google Scholar
  • Heckman JJ, Vytlacil E (2005) Structural equations, treatment effects, and econometric policy evaluation. Econometrica 73(3):669–738.CrossrefGoogle Scholar
  • Hirano K, Porter JR (2009) Asymptotics for statistical treatment rules. Econometrica 77(5):1683–1701.CrossrefGoogle Scholar
  • Hudgens MG, Halloran ME (2008) Toward causal inference with interference. J. Amer. Statist. Assoc. 103(482):832–842.CrossrefGoogle Scholar
  • Immorlica N, Mao J, Slivkins A, Wu ZS (2020) Incentivizing exploration with selective data disclosure. Proc. 21st ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 647–648.Google Scholar
  • Izzo Z, Ying L, Zou J (2021) How to learn when data reacts to your model: Performative gradient descent. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., vol. 139 (PMLR, New York), 4641–4650.Google Scholar
  • Kallus N, Zhou A (2021) Minimax-optimal policy learning under unobserved confounding. Management Sci. 67(5):2870–2890.LinkGoogle Scholar
  • Kallus N, Mao X, Wang K, Zhou Z (2022) Doubly robust distributionally robust off-policy evaluation and learning. Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S, eds. Proc. 39th Internat. Conf. Machine Learn., vol. 162 (PMLR, New York), 10598–10632.Google Scholar
  • Kitagawa T, Tetenov A (2018) Who should be treated? Empirical welfare maximization methods for treatment choice. Econometrica 86(2):591–616.CrossrefGoogle Scholar
  • Kleinberg J, Raghavan M (2020) How do classifiers induce agents to invest effort strategically? ACM Trans. Econom. Comput. 8(4):1–23.CrossrefGoogle Scholar
  • Lei L, Sahoo R, Wager S (2023) Policy learning under biased sample selection. Preprint, submitted April 23, https://arxiv.org/abs/2304.11735.Google Scholar
  • Letham B, Karrer B, Ottoni G, Bakshy E (2019) Constrained Bayesian optimization with noisy experiments. Bayesian Anal. 14(2):495–519.CrossrefGoogle Scholar
  • Liao L, Kroer C (2023) Statistical inference and A/B testing for first-price pacing equilibria. Preprint, submitted June 29, https://arxiv.org/abs/2301.02276.Google Scholar
  • Luenberger DG (1969) Optimization by Vector Space Methods (Wiley and Sons, New York).Google Scholar
  • Manski CF (2004) Statistical treatment rules for heterogeneous populations. Econometrica 72(4):1221–1246.CrossrefGoogle Scholar
  • Manski CF (2011) Choosing treatment policies under ambiguity. Annual Rev. Econom. 3(1):25–49.CrossrefGoogle Scholar
  • Mansour Y, Slivkins A, Syrgkanis V (2015) Bayesian incentive-compatible bandit exploration. Proc. 16th ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 565–582.Google Scholar
  • Mansour Y, Slivkins A, Syrgkanis V, Wu ZS (2022) Bayesian exploration: Incentivizing exploration in Bayesian games. Oper. Res. 70(2):1105–1127.LinkGoogle Scholar
  • Miller J, Milli S, Hardt M (2020) Strategic classification is causal modeling in disguise. Daumé III H, Singh A, eds. Proc. 37th Internat. Conf. Machine Learn. (PMLR, New York), 6917–6926.Google Scholar
  • Miller JP, Perdomo JC, Zrnic T (2021) Outside the echo chamber: Optimizing the performative risk. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn. (PMLR, New York), 7710–7720.Google Scholar
  • Mo W, Qi Z, Liu Y (2021) Learning optimal distributionally robust individualized treatment rules. J. Amer. Statist. Assoc. 116(534):659–674.CrossrefGoogle Scholar
  • Munro E, Wager S, Xu K (2023) Treatment effects in market equilibrium. Preprint, submitted January 11, 2021, https://arxiv.org/abs/2109.11647.Google Scholar
  • Myerson RB (1982) Optimal coordination mechanisms in generalized principal–agent problems. J. Math. Econom. 10(1):67–81.CrossrefGoogle Scholar
  • Offer-Westort M, Dimmery D (2021) Experimentation for homogenous policy change. Preprint, submitted January 28, https://arxiv.org/abs/2101.12318.Google Scholar
  • Perdomo J, Zrnic T, Mendler-Dünner C, Hardt M (2020) Performative prediction. Daumé III H, Singh A, eds. Proc. 37th Internat. Conf. Machine Learn. (PMLR, New York), 7599–7609.Google Scholar
  • Ross SA (1973) The economic theory of agency: The principal’s problem. Amer. Econom. Rev. 63(2):134–139.Google Scholar
  • Rossi PE, McCulloch RE, Allenby GM (1996) The value of purchase history data in target marketing. Marketing Sci. 15(4):321–340.LinkGoogle Scholar
  • Sävje F, Aronow P, Hudgens M (2021) Average treatment effects in the presence of unknown interference. Ann. Statist. 49(2):673–701.CrossrefGoogle Scholar
  • Si N, Zhang F, Zhou Z, Blanchet J (2023) Distributionally robust batch contextual bandits. Management Sci. 69(10):5772–5793.LinkGoogle Scholar
  • Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Pereira F, Burges CJ, Bottou L, Weinberger KQ, eds. Twenty-Sixth Annual Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY).Google Scholar
  • Spence M (2002) Signaling in retrospect and the informational structure of markets. Amer. Econom. Rev. 92(3):434–459.CrossrefGoogle Scholar
  • Srinivas N, Krause A, Kakade S, Seeger M (2010) Gaussian process optimization in the bandit setting: No regret and experimental design. Fürnkranz J, Joachims T, eds. Proc. 27th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 1015–1022.Google Scholar
  • Stiglitz JE (2002) Information and the change in the paradigm in economics. Amer. Econom. Rev. 92(3):460–501.CrossrefGoogle Scholar
  • Tchetgen Tchetgen EJ, VanderWeele TJ (2012) On causal inference in the presence of interference. Statist. Methods Medical Res. 21(1):55–75.CrossrefGoogle Scholar
  • Ugander J, Karrer B, Backstrom L, Kleinberg J (2013) Graph cluster randomization: Network exposure to multiple universes. Proc. 19th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 329–337.Google Scholar
  • VanderWeele TJ, Luedtke AR, van der Laan MJ, Kessler RC (2019) Selecting optimal subgroups for treatment using many covariates. Epidemiology 30(3):334–341.CrossrefGoogle Scholar
  • Vazquez-Bare G (2022) Identification and estimation of spillover effects in randomized experiments. J. Econometrics 237(1):105237.CrossrefGoogle Scholar
  • Viviano D (2022) Experimental design under network interference. Preprint, submitted July 20, https://arxiv.org/abs/2003.08421.Google Scholar
  • Williams CK, Rasmussen CE (2006) Gaussian Processes for Machine Learning (MIT Press).Google Scholar
  • Yang F (2022) Costly multidimensional screening. Preprint, submitted August 16, https://arxiv.org/abs/2109.00487.Google Scholar
  • Zhang DJ, Dai H, Dong L, Qi F, Zhang N, Liu X, Liu Z, Yang J, et al. (2018) How do price promotions affect customer behavior on retailing platforms? Evidence from a large randomized experiment on Alibaba. Production Oper. Management 27(12):2343–2345.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.