🤖 AI Summary
This study addresses the conflation in existing AI-based credit decision systems between direct discrimination and structural inequality mediated through financial features. Building on Pearl’s causal framework, the authors propose a method to identify natural direct and indirect effects under weaker assumptions in the presence of treatment-induced confounding, and derive conservative bounds for the otherwise non-identifiable natural effects. To account for residual confounding along the direct pathway, they integrate E-value sensitivity analysis. A doubly robust augmented inverse probability weighting (AIPW) estimator with cross-fitting is employed to achieve semiparametric efficiency, and the accompanying CausalFair toolkit is publicly released. Applied to 89,465 mortgage applications from New York State in 2022, the analysis reveals that of the 7.9-percentage-point racial denial gap, at least 77% stems from structural inequality, while direct discrimination accounts for no more than a conservative lower bound of 23%.
📝 Abstract
Statistical fairness metrics in AI-driven credit decisions conflate two causally distinct mechanisms: discrimination operating directly from a protected attribute to a credit outcome, and structural inequality propagating through legitimate financial features. We formalise this distinction using Pearl's framework of natural direct and indirect effects applied to the credit decision setting. Our primary theoretical contribution is an identification strategy for natural direct and indirect effects under treatment-induced confounding -- the prevalent setting in which protected attributes causally affect both financial mediators and the final decision, violating standard sequential ignorability. We show that interventional direct and indirect effects (IDE/IIE) are identified under the weaker Modified Sequential Ignorability assumption, and prove that IDE/IIE provide conservative bounds on the unidentified natural effects under monotone indirect treatment response. We propose a doubly-robust augmented inverse probability weighted (AIPW) estimator for IDE/IIE with semiparametric efficiency properties, implemented via cross-fitting. An E-value sensitivity analysis addresses residual confounding on the direct pathway. Empirical evaluation on 89,465 real HMDA conventional purchase mortgage applications from New York State (2022) demonstrates that approximately 77% of the observed 7.9 percentage-point racial denial disparity operates through financial mediators shaped by structural inequality, while the remaining 23% constitutes a conservative lower bound on direct discrimination. The open-source CausalFair Python package implements the full pipeline for deployment at resource-constrained financial institutions.