Proxy-Aided Demand Learning with an Application to Various Pricing Problems
References
- (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.Link, Google Scholar
- (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Link, Google Scholar
- (2023) Proximal reinforcement learning: Efficient off-policy evaluation in partially observed Markov decision processes. Oper. Res. 72(3):1071–1086.Link, Google Scholar
- (2018) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.Google Scholar
- (2014) Constrained Optimization and Lagrange Multiplier Methods (Academic Press, New York).Google Scholar
- (2023) The power and limits of predictive approaches to observational data-driven optimization: The case of pricing. INFORMS J. Optim. 5(1):110–129.Link, Google Scholar
- (2017) Data-driven learning in dynamic pricing using adaptive optimization. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.Link, Google Scholar
- (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.Link, Google Scholar
- (2010) Testing the validity of a demand model: An operations perspective. Manufacturing Service Oper. Management 12(1):162–183.Link, Google Scholar
- (2024) Demand and welfare analysis in discrete choice models with social interactions. Rev. Econom. Stud. 91(2):748–784.Crossref, Google Scholar
- (2005) New empirical generalizations on the determinants of price elasticity. J. Marketing Res. 42(2):141–156.Crossref, Google Scholar
- (2019) Unbiased multilevel Monte Carlo: Stochastic optimization, steady-state simulation, quantiles, and other applications. Preprint, submitted April 22, https://arxiv.org/abs/1904.09929.Google Scholar
- (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.Link, Google Scholar
- (2023) Offline pricing and demand learning with censored data. Management Sci. 69(2):885–903.Link, Google Scholar
- (2023) Jump interval-learning for individualized decision making with continuous treatments. J. Machine Learn. Res. 24(140):1–92.Google Scholar
- (2023) Robust dynamic pricing with demand learning in the presence of outlier customers. Oper. Res. 71(4):1362–1386.Link, Google Scholar
- (2024) Proximal causal inference with text data. Globerson A, Mackey L, Belgrave D, Fan A, Paquet U, Tomczak J, Zhang C, eds. Proc. 38th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 135983–136017.Google Scholar
- (2019) Coordinating pricing and inventory replenishment with nonparametric demand learning. Oper. Res. 67(4):1035–1052.Abstract, Google Scholar
- (2021) Nonparametric learning algorithms for joint pricing and inventory control with lost sales and censored demand. Math. Oper. Res. 46(2):726–756.Link, Google Scholar
- (2020) Data-based dynamic pricing and inventory control with censored demand and limited price changes. Oper. Res. 68(5):1445–1456.Link, Google Scholar
- (2022) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.Link, Google Scholar
- (2016) Personalized dose finding using outcome weighted learning. J. Amer. Statist. Assoc. 111(516):1509–1521.Crossref, Google Scholar
- (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
- (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.Link, Google Scholar
- (2021) Dynamic pricing with fairness constraints. Preprint, submitted September 25, https://doi.org/10.2139/ssrn.3930622.Google Scholar
- (2017) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.Link, Google Scholar
- (2024) Semiparametric proximal causal inference. J. Amer. Statist. Assoc. 119(546):1348–1359.Crossref, Google Scholar
- (2002) Convergence properties of the BFGS algorithm. SIAM J. Optim. 13(3):693–701.Crossref, Google Scholar
- (2022) Dynamic pricing with demand learning and reference effects. Management Sci. 68(10):7112–7130.Link, Google Scholar
- (2020) Minimax estimation of conditional moment models. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Proc. 34th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 12248–12262.Google Scholar
- (2023) Stochastic approximation for expectation objective and expectation inequality-constrained nonconvex optimization. Preprint, submitted July 6, https://arxiv.org/abs/2307.02943.Google Scholar
- (2022) Policy optimization using semiparametric models for dynamic pricing. J. Amer. Statist. Assoc. 119(545):552–564.Crossref, Google Scholar
- (2023) Partial identification of causal effects using proxy variables. Preprint, submitted April 10, https://arxiv.org/abs/2304.04374.Google Scholar
- (2022) Minimax kernel machine learning for a class of doubly robust functionals with application to proximal causal inference. Camps-Valls G, Ruiz FJR, Valera I, eds. Proc. 25th Internat. Conf. Artificial Intelligence Statist., Proceedings of the Machine Learning Research, vol. 151 (PMLR, New York), 7210–7239.Google Scholar
- (2023) Incentive-aware contextual pricing with non-parametric market noise. Ruiz F, Dy J, van de Meent J-W, eds. Proc. 26th Internat. Conf. Artificial Intelligence Statist., Proceedings of the Machine Learning Research, vol. 206 (PMLR, New York), 9331–9361.Google Scholar
- (2019) Top challenges from the first practical online controlled experiments summit. ACM SIGKDD Explorations Newsletter 21(1):20–35.Crossref, Google Scholar
- (2017) Deep IV: A flexible approach for counterfactual prediction. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn., Proceedings of the Machine Learning Research, vol. 70 (PMLR, New York), 1414–1423.Google Scholar
- (2020) Causal Inference: What If (Chapman & Hall/CRC, Boca Raton, FL).Google Scholar
- (1983) On the “law of demand.” Econometrica 51(4):997–1019.Crossref, Google Scholar
- (2016) Large-scale price optimization via network flow. Lee DD, von Luxburg U, Garnett R, Sugiyama M, Guyon I, eds. Proc. 30th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 3862–3870.Google Scholar
- (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
- (2018) Policy evaluation and optimization with continuous treatments. Storkey A, Perez-Cruz F, eds. Proc. 21st Internat. Conf. Artificial Intelligence Statist., Proceedings of the Machine Learning Research, vol. 84 (PMLR, New York), 1243–1251.Google Scholar
- (2021) Causal inference under unmeasured confounding with negative controls: A minimax learning approach. Preprint, submitted March 25, https://arxiv.org/abs/2103.14029.Google Scholar
- (2021) Adaptive supply chain: Demand-supply synchronization using deep reinforcement learning. Algorithms 14(8):240.Crossref, Google Scholar
- (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.Link, Google Scholar
- (2022) Deep learning methods for proximal inference via maximum moment restriction. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. Proc. 36th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 11189–11201.Google Scholar
- (1989) Linear Integral Equations, Applied Mathematical Sciences, vol. 82 (Springer, Berlin).Crossref, Google Scholar
- (2014) Measurement bias and effect restoration in causal inference. Biometrika 101(2):423–437.Crossref, Google Scholar
- (1999) Manufacturer’s pricing strategy and return policy for a single-period commodity. Eur. J. Oper. Res. 116(2):291–304.Crossref, Google Scholar
- (2012) Newsvendor-type models with decision-dependent uncertainty. Math. Methods Oper. Res. 76(2):189–221.Crossref, Google Scholar
- (2023) Dynamic pricing with external information and inventory constraint. Management Sci. 70(9):5985–6001.Google Scholar
- (2006) Dynamic pricing with real-time demand learning. Eur. J. Oper. Res. 174(1):522–538.Crossref, Google Scholar
- (2019) Stochastic successive convex approximation for non-convex constrained stochastic optimization. IEEE Trans. Signal Processing 67(16):4189–4203.Crossref, Google Scholar
- (2024) Regression-based proximal causal inference. Amer. J. Epidemiology 194(7):2030–2036.Crossref, Google Scholar
- (2025) Contextual dynamic pricing with strategic buyers. J. Amer. Statist. Assoc. 120(550):896–908.Crossref, Google Scholar
- (2024) Distribution-free contextual dynamic pricing. Math. Oper. Res. 49(1):599–618.Link, Google Scholar
- (2021) Proximal causal learning with kernels: Two-stage estimation and moment restriction. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., Proceedings of the Machine Learning Research, vol. 139 (PMLR, New York), 7512–7523.Google Scholar
- (2018) Identifying causal effects with proxy variables of an unmeasured confounder. Biometrika 105(4):987–993.Crossref, Google Scholar
- (2023) Personalized pricing with invalid instrumental variables: Identification, estimation, and policy learning. Preprint, submitted February 24, https://arxiv.org/abs/2302.12670.Google Scholar
- (2024) A confounding bridge approach for double negative control inference on causal effects. Statist. Theory Related Fields 8(4):262–273.Crossref, Google Scholar
- (2009) Causal inference in statistics: An overview. Statist. Surveys 3:96–146.Crossref, Google Scholar
- (2023) Dynamic pricing with unknown nonparametric demand and limited price changes. Oper. Res. 72(6):2726–2744.Link, Google Scholar
- (2024) Proximal learning for individualized treatment regimes under unmeasured confounding. J. Amer. Statist. Assoc. 119(546):915–928.Crossref, Google Scholar
- (2011) Performance guarantees for individualized treatment rules. Ann. Statist. 39(2):1180–1210.Crossref, Google Scholar
- (2025) Adaptive proximal causal inference with some invalid proxies. Preprint, submitted July 25, https://www.arxiv.org/abs/2507.19623.Google Scholar
- (2019) Semi-parametric dynamic contextual pricing. Wallach HM, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox EB, eds. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 2363–2373.Google Scholar
- (2023) Optimal treatment regimes for proximal causal learning. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Proc. 37th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 47735–47748.Google Scholar
- (2019) Kernel instrumental variable regression. Wallach HM, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox EB, eds. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 4593–4605.Google Scholar
- (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.Link, Google Scholar
- (2025) Offline feature-based pricing under censored demand: A causal inference approach. Manufacturing Service Oper. Management 27(2):535–553.Link, Google Scholar
- (2024) An introduction to proximal causal inference. Statist. Sci. 39(3):375–390.Crossref, Google Scholar
- (2022) Blessing from experts: Super reinforcement learning in confounded environments. Preprint, submitted September 29, https://arxiv.org/abs/2209.15448.Google Scholar
- (2021) Uncertainty quantification for demand prediction in contextual dynamic pricing. Production Oper. Management 30(6):1703–1717.Crossref, Google Scholar
- (2023) Online regularization toward always-valid high-dimensional dynamic pricing. J. Amer. Statist. Assoc. 119(548):2895–2907.Crossref, Google Scholar
- (2023) Doubly robust proximal causal learning for continuous treatments. 12th Internat. Conf. Learn. Representations (Vienna).Google Scholar
- (2008) Customized bundle pricing for information goods: A nonlinear mixed-integer programming approach. Management Sci. 54(3):608–622.Link, Google Scholar
- (2024) On identification of dynamic treatment regimes with proxies of hidden confounders. Preprint, submitted February 22, https://arxiv.org/abs/2402.14942.Google Scholar
- (2012) A robust method for estimating optimal treatment regimes. Biometrics 68(4):1010–1018.Crossref, Google Scholar
- (2024) Positivity-free policy learning with observational data. Dasgupta S, Mandt S, Li Y, eds. Proc. 27th Internat. Conf. Artificial Intelligence Statist., Proceedings of the Machine Learning Research, vol. 238 (PMLR, New York), 1918–1926.Google Scholar
- (2012) Estimating individualized treatment rules using outcome weighted learning. J. Amer. Statist. Assoc. 107(499):1106–1118.Crossref, Google Scholar
- (2019) An optimal algorithm for stochastic and adversarial bandits. Chaudhuri K, Sugiyama M, eds. Proc. 22nd Internat. Conf. Artificial Intelligence Statist., Proceedings of the Machine Learning Research, vol. 89 (PMLR, New York), 467–475.Google Scholar

