Ravi Kumar, Shahin Boluki, Karl Isler, Jonas Rauch, Darius Walczak
arXiv 4 May 2022 · Statistics — Machine Learning · 2 citations (OpenAlex)
arXiv:2205.01875 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated machine learning (ML) techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates due to regularization. To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model. In the first-stage we construct estimators of observed purchases and prices given the feature vector using sophisticated ML estimators such as deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from the Airline industry. Our numerical studies demonstrate that our proposed two-stage approach reduces the estimation error in price-sensitivity parameters from 25% to 4% in realistic simulation settings. The two-stage estimation techniques proposed in this work allows practitioners to leverage modern ML techniques to robustly estimate price-sensitivities while still maintaining interpretability and allowing ease of validation of its various constituent parts.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 11 | 2 | 100% |
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| 3 | D. Nekipelov, V. Semenova, and V. Syrgkanis, “Regularised orthogonal… (2022) Regularised orthogonal machine learning for nonlinear semiparametric models | 0.737 | 3 | 2 | 100% |
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| 8 | J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy, “Deep iv: A fl… (2017) Deep iv: A flexible approach for counterfactual prediction | 0.585 | 3 | 1 | 100% |
| 9 | R. S. Sutton and A. G. Barto, EnglishReinforcement learning. An intr… (2018) | 0.511 | 2 | 2 | 50% |
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