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Shapley Values

Shapley Values, which originate from cooperative game theory, are used to identify the contributions of individual variables to the overall prediction made by a predictive model. Shapley Values provide an interpretable way of explaining complex models, such as decision trees and neural networks, which are usually harder to understand compared to traditional regression models. Shapley values are considered the state of the art in computing drivers of disparity because they provide consistent explanations for predictions across different instances in the dataset, take into account the interaction effects between variables, and can be applied to a wide range of predictive models, not just linear regression, making them more versatile in addressing different types of predictive tasks.