揭示梯度提升解释中的首动偏差，提出有效解决方法。
First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution
- 通过分解残差路径发现特征重要性受顺序依赖影响
- 新方法DASH在高相关性下稳定度达0.977，优于单模型的0.938
- 适合需要可解释性稳定性的机器学习应用者
我们识别出首动偏差——梯度提升中序列残差拟合导致的SHAP特征重要性路径依赖集中——是多重共线性下归因不稳定的机制因素。单一模型规模扩大反而加剧该效应：与本文方法树总数相当的大单模型在所有测试方法中重现性最差。模型独立性可基本消除首动偏差。DASH（SHAP差异化聚合）和简单随机重训练均通过打破序列依赖恢复稳定性。当相关系数rho=0.9时，两者稳定度均达~0.977，而最优单模型降至0.958，大单模型更跌至0.938。在乳腺癌数据集上，DASH将稳定度从0.376提升至0.925（+0.549），优于随机重训练+0.063。非线性生成模型中，优势在rho≥0.7时显现。DASH提供特征稳定指数与重要性-稳定性图两种诊断工具，无需真实标签即可检测首动偏差。交叉ANOVA结合正式F统计量验证机制：DASH使方差来源从模型主导（40.6%）转为数据主导（73.6%）。代码见https://github.com/DrakeCaraker/dash-shap。
原文摘要 · Abstract (English)
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
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