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

TL;DR

This paper examines how selection bias affects the identification of bid-response functions in personalized loan pricing, demonstrating that causal machine learning methods can mitigate bias better than traditional models.

Contribution

It introduces a causal inference framework for loan pricing and evaluates the effectiveness of causal machine learning methods in overcoming selection bias in observational data.

Findings

Traditional models like logistic regression are adversely affected by selection bias.

Causal machine learning methods outperform conventional approaches in biased pricing data.

State-of-the-art causal methods can effectively address selection bias in loan pricing applications.

Abstract

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.