Rui Miao, Zhengling Qi, Cong Shi, Lin Lin
arXiv 24 Feb 2023 · Statistics — Methodology · 4 citations (OpenAlex)
arXiv:2302.12670 · PDF · DOI · OpenAlex · Extracted main text
Pricing based on individual customer characteristics is widely used to maximize sellers' revenues. This work studies offline personalized pricing under endogeneity using an instrumental variable approach. Standard instrumental variable methods in causal inference/econometrics either focus on a discrete treatment space or require the exclusion restriction of instruments from having a direct effect on the outcome, which limits their applicability in personalized pricing. In this paper, we propose a new policy learning method for Personalized pRicing using Invalid iNsTrumental variables (PRINT) for continuous treatment that allow direct effects on the outcome. Specifically, relying on the structural models of revenue and price, we establish the identifiability condition of an optimal pricing strategy under endogeneity with the help of invalid instrumental variables. Based on this new identification, which leads to solving conditional moment restrictions with generalized residual functions, we construct an adversarial min-max estimator and learn an optimal pricing strategy. Furthermore, we establish an asymptotic regret bound to find an optimal pricing strategy. Finally, we demonstrate the effectiveness of the proposed method via extensive simulation studies as well as a real data application from an US online auto loan company.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, Xiaohong and Demian Pouzo (2012) Estimation of nonparametric conditional moment models with possibly nonsmooth generalized residuals | 0.874 | 5 | 2 | 100% |
| 2 | Ai, Chunrong and Xiaohong Chen (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.843 | 3 | 3 | 100% |
| 3 | Ban, Gah-Yi and N Bora Keskin (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity | 0.811 | 4 | 2 | 100% |
| 4 | Blundell-Wignall, Adrian, Marianne Gizycki, et al (1992) Credit supply and demand and the Australian economy | 0.737 | 3 | 2 | 100% |
| 5 | Phillips, Robert, A Serdar Simsek, and Garrett Van Ryzin (2015) The effectiveness of field price discretion: Empirical evidence from auto lending | 0.737 | 3 | 2 | 100% |
| 6 | Kallus, Nathan and Angela Zhou (2018) Policy evaluation and optimization with continuous treatments, in | 0.693 | 5 | 1 | 100% |
| 7 | Chen, Guanhua, Donglin Zeng, and Michael R Kosorok (2016) Personalized dose finding using outcome weighted learning | 0.644 | 2 | 2 | 100% |
| 8 | Newey, Whitney K and James L Powell (2003) Instrumental variable estimation of nonparametric models | 0.644 | 2 | 2 | 100% |
| 9 | Lewbel, Arthur (2012) Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models | 0.585 | 3 | 1 | 100% |
| 10 | Tchetgen Tchetgen, Eric, BaoLuo Sun, and Stefan Walter (2021) The GENIUS approach to robust Mendelian randomization inference | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 59 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quantile-Optimal Policy Learning under Unmeasured Confounding | 0.511 | 2 | 2 |