<ul><li>Shapley values are important for explaining the impact of features on machine learning model decisions.</li><li>Exact computation of Shapley values is challenging and often requires a large number of model evaluations.</li><li>A unified framework has been developed for estimating Shapley values, including KernelSHAP and related estimators, with and without replacement sampling strategies.</li><li>The framework offers strong non-asymptotic theoretical guarantees and has been validated through benchmarking, showing low mean squared error and scalability to high-dimensional datasets.</li></ul>

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

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