# Debiasing Alternative Data for Credit Underwriting Using Causal Inference

arXiv: https://arxiv.org/abs/2410.22382 · HTML: https://mlsanity.com/paper/2410.22382/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [The Sensitivity of Counterfactual Fairness to Unmeasured Confounding](https://mlsanity.com/paper/1907.01040/) (2019)
- [Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms](https://mlsanity.com/paper/2404.14050/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Systems Thinking Approach to Algorithmic Fairness](https://mlsanity.com/paper/2412.16641/) (2024)

## Abstract (arXiv)

Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness, which could help expand credit access to underserved groups and lower costs for borrowers. But some forms of alternative data have historically been excluded from credit underwriting because it could act as an illegal proxy for a protected class like race or gender, causing redlining. We propose a method for applying causal inference to a supervised machine learning model to debias alternative data so that it might be used for credit underwriting. We demonstrate how our algorithm can be used against a public credit dataset to improve model accuracy across different racial groups, while providing theoretically robust nondiscrimination guarantees.