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In lending, where prices are specific to both customers and products, havinga well-functioning personalized pricing policy in place is essential toeffective business making. Typically, such a policy must be derived fromobservational 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 posingpricing as a problem of causal inference. Specifically, we consider thereaction of a customer to price a treatment effect. In our experiments, wesimulate varying levels of selection bias on a semi-synthetic dataset onmortgage loan applications in Belgium. We investigate the potential ofparametric and nonparametric methods for the identification of individualbid-response functions. Our results illustrate how conventional methods such aslogistic regression and neural networks suffer adversely from selection bias.In contrast, we implement state-of-the-art methods from causal machine learningand show their capability to overcome selection bias in pricing data.

A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions