Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
arXiv 7 Sep 2023 · Machine Learning
arXiv:2309.03730 · PDF · DOI · OpenAlex · Extracted main text
In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typically, such a policy must be derived from observational data, which introduces several challenges. While the problem of “endogeneity” is prominently studied in the established pricing literature, the problem of selection bias (or, more precisely, bid selection bias) is not. We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. Specifically, we consider the reaction of a customer to price a treatment effect. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We investigate the potential of parametric and nonparametric methods for the identification of individual bid-response functions. Our results illustrate how conventional methods such as logistic regression and neural networks suffer adversely from selection bias. In contrast, we implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bica, I., Jordon, J., van der Schaar, M (2020) Estimating the effects of continuous-valued interventions using generative adversarial networks | 1.000 | 7 | 3 | 100% |
| 2 | Schwab, P., Linhardt, L., Bauer, S., Buhmann, J.M., Karlen, W (2020) Learning counterfactual representations for estimating individual dose-response curves | 1.000 | 7 | 3 | 100% |
| 3 | Nie, L., Ye, M., Liu, Q., Nicolae, D (2021) Vcnet and functional targeted regularization for learning causal effects of continuous treatments | 1.000 | 5 | 3 | 100% |
| 4 | Vanderschueren, T., Boute, R., Verdonck, T., Baesens, B., Verbeke, W (2023) Optimizing the preventive maintenance frequency with causal machine learning self | 1.000 | 5 | 3 | 100% |
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| 8 | Berrevoets, J., Jordon, J., Bica, I., van der Schaar, M., et al (2020) Organite: Optimal transplant donor organ offering using an individual treatment effect | 0.644 | 2 | 2 | 100% |
| 9 | Phillips, R., Simsek, A.S., Van Ryzin, G (2012) Endogeneity and price sensitivity in customized pricing | 0.644 | 2 | 2 | 100% |
| 10 | Phillips, R (2013) Optimizing prices for consumer credit | 0.644 | 2 | 2 | 100% |
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