Paper Overview
- Field: Machine Learning
- Author: Emanuele Luzio
- Published: 2026-08-19
- arXiv: 2608.19127
Summary
A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate results, and every instance becomes a point in R^M on which the model acts linearly: the score is the sum of the coordinates. This small change of view makes contrastive explanation exact. The difference between two instances is a vector that is identically zero wherever they share a leaf, so the gap between a rejected applicant and an accepted one is carried by a handful of coordinates, each traceable to a real split in a real tree. Nothing is fitted, sampled, or assumed additive in features — the additivity is already there, in the right space.
The authors build a recourse method on this representation and evaluate it on five tabular datasets under repeated cross-validation. The proposed recourses reconstruct the model's own decisions to 6.2 × 10^-15, so auditors can recheck the arithmetic without access to the model. On a credit dataset, the method is Pareto-nondominated in the effort-vs-realism tradeoff. When recourses are restricted to changes a subject can actually make — not their age, not resolved delinquencies — it retains 58% effectiveness, while the strongest baseline retains 41%. Standard evaluation misses this distinction because it never asks whether a recourse is actionable.
*Auto-collected on 2026-08-21*