Shapley Value Estimation for Multi-Site Data with Blockwise-Missing Features
Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley estimators generally assume that observations used to evaluate the coalitional game are fully observed under a common feature space. This assumption is routinely violated in multi-site studies across biomedicine, social science, and environmental monitoring, where institutions record different features under different protocols, producing systematic blockwise missingness across sources. We first show that the standard remedy of imputing missing features before computing Shapley values introduces systematic, coalition-dependent bias into the resul...
| Authors | Siqi Li, Wangxuan Fan, Yiming Li et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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