ArticleNature communications2022
Explaining a series of models by propagating Shapley values.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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Who cites it
91 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population.Lipids in health and disease · 2026Article
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- Development and validation of a risk stratification model for sarcopenia in patients with chronic lung disease: a cross-sectional study based on CHARLS data.BMJ open respiratory research · 2026Article
- The exposome of brain aging across 34 countries.Nature medicine · 2026Article
- SensX: Model-Agnostic Local Feature Attribution via Calibrated Global Sensitivity Analysis.Transactions on machine learning research · 2026Article
- Machine learning models for predicting childhood anemia in Mozambique: analysis from national survey data.BMC pediatrics · 2026Article
- Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review.Toxics · 2026Review
- Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions.Scientific reports · 2026Article
- Clinically interpretable extracellular vesicle gene model for Non-Invasive liver cancer diagnosis.Scientific reports · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution. The latter is particularly important because it often arises in finance where explanations are mandated. Here, we present Generalized DeepSHAP (G-DeepSHAP), a tractable method to propagate local feature attributions through complex series of models based on a connection to the Shapley value. We evaluate G-DeepSHAP across biological, health, and financial datasets to show that it provides equally salient explanations an order of magnitude faster than existing model-agnostic attribution techniques and demonstrate its use in an important distributed series of models setting.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.