Bockel-Rickermann, ChristopherChristopherBockel-RickermannVerboven, SamSamVerbovenVerdonck, TimTimVerdonckVerbeke, WouterWouterVerbeke2026-06-162026-06-1620250167-9236https://imec-publications.be/handle/20.500.12860/59730Personal loan pricing requires accurate estimates of individual customer behavior, such as the willingness to take out a loan at a given price, the “bid response”. This is challenging due to the nonlinearity of responses hindering the discretionary definition of models, as well as the confoundedness of observational training data. This paper investigates the application of data-driven and machine learning (ML) methods to estimate individual bid responses. We argue that framing bid response modeling as a problem of causal inference is crucial for accurate modeling and understanding of challenging factors. We test established ML algorithms and state-of-the-art causal ML methods on a dataset on mortgage loan applications in Belgium and investigate the effects of different levels of confounding in the data. Our results demonstrate that methods that address confounding can improve bid response estimation, especially when established non-causal methods are negatively affected.enPROPENSITY SCOREINTEREST-RATESINFERENCEMODELSNETWORKSPRICESCOSTSML in bankingCausal machine learningPricingConfounding biasCausal inferenceTreatment effect estimationScience & TechnologyTechnologyJournal article10.1016/j.dss.2024.114378WOS:001397881800001