First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution

TL;DR

This paper investigates first-mover bias in gradient boosting explanations, demonstrating how it causes attribution instability under multicollinearity and proposing methods like DASH and seed-averaging to improve stability.

Contribution

It identifies the mechanistic cause of attribution instability, introduces DASH for bias mitigation, and provides diagnostic tools for detecting first-mover bias.

Findings

Scaling up a single model worsens attribution reproducibility.

DASH and seed-averaging significantly improve stability under high correlation.

DASH outperforms stochastic retraining on real and simulated data.

Abstract

We identify first-mover bias -- path-dependent concentration of SHAP feature importance from sequential residual fitting in gradient boosting -- as a mechanistic contributor to attribution instability under multicollinearity. Scaling up a single model amplifies this effect: a Large Single Model matching our method's total tree count produces the poorest attribution reproducibility of any approach tested. We show that model independence largely neutralizes first-mover bias. Both DASH (Diversified Aggregation of SHAP) and simple seed-averaging (Stochastic Retrain) restore stability by breaking the sequential dependency chain. At rho=0.9, both achieve stability ~0.977, while Single Best degrades to 0.958 and LSM to 0.938. On Breast Cancer, DASH improves stability from 0.376 to 0.925 (+0.549), outperforming Stochastic Retrain by +0.063. Under nonlinear DGPs, the advantage emerges at rho>=0.7. DASH provides two diagnostic tools -- the Feature Stability Index and Importance-Stability Plot -- that detect first-mover bias without ground truth. A crossed ANOVA with formal F-statistics confirms the mechanism: DASH shifts variance from model-dominated (40.6%) to data-dominated (73.6%). Software at https://github.com/DrakeCaraker/dash-shap