# Learning Recourse Costs from Pairwise Feature Comparisons

arXiv: https://arxiv.org/abs/2409.13940 · HTML: https://mlsanity.com/paper/2409.13940/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 20 Sep 2024.

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

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [FACE: Feasible and Actionable Counterfactual Explanations](https://mlsanity.com/paper/1909.09369/) (2019)
- [Learning Model-Agnostic Counterfactual Explanations for Tabular Data](https://mlsanity.com/paper/1910.09398/) (2019)
- [Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems](https://mlsanity.com/paper/1907.09615/) (2019)
- [How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions](https://mlsanity.com/paper/1609.00070/) (2016)

## Built on by (most cited first, 2 of 2)

- [Designing User-Centric Metrics for Evaluation of Counterfactual Explanations](https://mlsanity.com/paper/2507.15162/) (2025)
- [Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan](https://mlsanity.com/paper/2607.01610/) (2026)

## Abstract (arXiv)

This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associating each feature to its cost of modification. Since it is hard to obtain such costs by directly surveying humans, in this paper, we propose the use of the Bradley-Terry model to automatically infer feature-wise costs using non-exhaustive human comparison surveys. We propose that users only provide inputs comparing entire recourses, with all candidate feature modifications, determining which recourses are easier to implement relative to others, without explicit quantification of their costs. We demonstrate the efficient learning of individual feature costs using MAP estimates, and show that these non-exhaustive human surveys, which do not necessarily contain data for each feature pair comparison, are sufficient to learn an exhaustive set of feature costs, where each feature is associated with a modification cost.