A Causal Perspective on Loan Pricing: Investigating the Impacts of
  Selection Bias on Identifying Bid-Response Functions
Christopher Bockel-Rickermann Sam Verboven Tim Verdonck Wouter Verbeke
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.