DeepSHAP

DeepSHAP is a method for attributing the predictions of deep neural networks to their input features. It combines the theoretical framework of SHAP (Shapley Additive exPlanations) with the efficient backpropagation-based approach of DeepLIFT to produce feature attributions for complex models. The aim is to provide explanations that are locally accurate, consistent, and missingness-aware while remaining computationally feasible for deep architectures.

The method relies on a background dataset that represents plausible inputs as a reference. For a given input, DeepSHAP estimates the contribution of each feature by integrating information from multiple reference samples and propagating attribution signals backward through the network. By leveraging the fast, gradient-like propagation of DeepLIFT, DeepSHAP can compute approximate SHAP values without the combinatorial cost of exact Shapley value calculation. The resulting attributions are additive and can be interpreted as how much each input feature contributed to the model’s output relative to the reference.

In relation to other attribution methods, DeepSHAP provides a practical way to obtain SHAP-consistent explanations for deep models. It is commonly used in applications such as image classification, natural language processing, and other domains where neural networks are prominent. Limitations include dependence on the choice of background distribution and reference inputs, which can influence the attribution results. As with many attribution techniques, the explanations are approximations and can be sensitive to model architecture, training data, and preprocessing steps.