SHAP Attribution Analysis for ML Interpretability

• SHAP Attribution Analysis is a model-agnostic framework that uses Shapley values from cooperative game theory to reliably quantify individual feature contributions with guarantees of local accuracy, fairness, and consistency.
• It employs advanced computational techniques—such as Tree SHAP, Kernel SHAP, and Fourier-based approximations—to efficiently scale explanations in high-dimensional, time-series, and complex data settings.
• Extensions like Latent SHAP and Causal SHAP enhance human interpretability and causal inference, providing robust, real-time, and context-aware explanations for various machine learning models.

SHAP Attribution Analysis

SHAP (SHapley Additive exPlanations) is a model-agnostic framework for quantifying individual feature contributions in complex machine learning models. SHAP leverages the foundational Shapley value concept from cooperative game theory and adapts it to machine learning settings, attaining theoretical guarantees such as local accuracy, fairness, and consistency in additive explanation models. SHAP has become a central tool in interpretability research, with numerous computational variants, specialized algorithms, and analyses for issues such as scalability, attribution fidelity, robustness to distributional, model, or data uncertainty, and human interpretability (Bitton et al., 2022, Chen et al., 3 Apr 2025, Morales, 31 Oct 2025).

1. Mathematical Foundation and Exact Shapley Value Formulation

The classical SHAP formulation considers a model $f:\mathbb{R}^M \rightarrow \mathbb{R}$ and input $x\in\mathbb{R}^M$. Feature attributions, the SHAP values $\phi_i(x)$, are computed as the average marginal contribution of feature $i$ across all subsets $S$ not containing $i$:

$$\phi_i(x) = \sum_{S \subseteq \{1,\dots,M\} \setminus \{i\}} \frac{|S|!(M - |S| - 1)!}{M!} [f_{S \cup \{i\}}(x) - f_S(x)]$$

where $f_S(x)$ is the expected output conditional on features in $S$ fixed to their values in $x$ and the rest marginalized, typically using a background distribution drawn from the data generating process (Bitton et al., 2022, Lundberg et al., 2017). This formulation is the unique solution (within additive models) satisfying local accuracy (completeness), missingness, and consistency, as formalized by classical Shapley value axioms.

For predictive models on discrete or multi-valued input spaces, the recent spectral theory of SHAP introduces a Fourier expansion on an orthonormal tensor-product basis under a product probability measure, allowing decomposition of SHAP values as linear functionals of the model's Fourier coefficients. This yields explicit bounds on attribution stability and enables substantial acceleration by truncating high-degree or low-variance spectral components (Morales, 31 Oct 2025).

2. Computation and Scalability: Algorithms and Approximations

Direct SHAP evaluation is intractable for high-dimensional problems due to exponential scaling with the number of features. Several algorithmic strategies address this:

A. Tree SHAP for Tree Ensembles

The Tree SHAP algorithm exploits the structure of decision trees to propagate subset weights efficiently through the tree, achieving polynomial-time computation $O(TLD^2)$, where $T$ is the number of trees, $L$ the number of leaves, and $D$ the maximum depth. Tree SHAP preserves the completeness and consistency axioms and is integrated into mainstream gradient-boosting packages (Lundberg et al., 2017).

B. Kernel SHAP and Monte Carlo Variants

Kernel SHAP approximates the SHAP solution by sampling coalitions and solving a weighted least-squares regression. Various Monte Carlo schemes, including permutation and truncated sampling, are used to reduce the required model evaluations (Chen et al., 3 Apr 2025).

C. Patch-wise, Segment-wise, and Domain-wise SHAP

For high-dimensional data (e.g., time series, images, signals), features can be aggregated into contiguous or semantically meaningful "patches" or "segments," dramatically reducing the feature space cardinality at the expense of granularity (Chen et al., 3 Apr 2025, Serramazza et al., 3 Sep 2025). The selection of segmentation method (equal-length, clustering, agglomerative, or data-adaptive) and segment count is crucial; equal-length segmentation usually provides superior or comparable explanation fidelity for time series (Serramazza et al., 3 Sep 2025).

D. SHapley Estimated Explanation (SHEP)

SHEP is a linear-time approximation that computes only two marginal expectations per feature: the effect when the feature is present and when it is absent, then averages them. It retains high attribution fidelity ($>0.85$ cosine similarity with exact SHAP), enables real-time post-hoc explanations, and is robust for coarse-grained patches (Chen et al., 3 Apr 2025).

E. Fourier-SHAP/Surrogate Approximations

Fourier-based surrogates reconstruct SHAP attributions by computing a truncated generalized Fourier expansion, providing orders-of-magnitude speedups with negligible loss in attribution quality, particularly suitable for tabular, categorical, or binned features (Morales, 31 Oct 2025).

3. Extensions for Human Interpretable and Causal Explanations

A. Latent SHAP and Non-Invertible Mappings

Latent SHAP addresses interpretability when features are encoded, processed, or entangled such that an invertible mapping to human-readable variables does not exist. By constructing a surrogate ("latent background set") relating the model output in the native feature space to points in a learned or domain-provided human-interpretable space, SHAP attributions are computed via kernel regression in this new domain (Bitton et al., 2022). Latent SHAP can produce coherent, concise verbal explanations even when only feature abstractions are reliably available.

B. Causal SHAP

Causal SHAP integrates constraint-based causal discovery (the PC algorithm) and intervention calculus (IDA algorithm) to distinguish between truly causal and merely correlated features. It modifies the classical Shapley kernel by down-weighting or excluding features lacking a causal path to the target, improving attribution reliability in highly correlated or multicollinear settings and aligning explanations with structural causality (Ng et al., 31 Aug 2025).

C. Robustness to Distributional Uncertainty

SHAP attributions depend on the background reference distribution. Under ambiguity or estimation uncertainty, the SHAP score becomes a function over an uncertainty region of distributions; extremal attribution intervals admit tight computation at the hypercube vertices of the uncertainty region, but can be sensitive, unstable, and are NP-complete for general models, including decision trees (Cifuentes et al., 23 Jan 2024).

4. Advanced Applications, Practical Workflows, and Theoretical Insights

A. Instance Attribution and Data-Centric SHAP

SHAP can be applied to assign importance not only to input features but also to individual training instances (instance attribution). Kernel-based surrogates approximating Shapley instance scores enable scalable, fine-tuning-free analysis of data importance, such as FreeShap using neural tangent kernels, providing higher robustness (lower probability of sign flip under data resampling) than leave-one-out and effective ranking for data removal, selection, or mislabel detection (Wang et al., 7 Jun 2024).

B. RAG and LLMs

In retrieval-augmented generation (RAG), document-level SHAP evaluates the marginal contribution of each retrieved document to the generation utility. Computation is limited by LLM call complexity: KernelSHAP and regression-based surrogates provide near-exact fidelity at $O(n^3)$ cost; leave-one-out is computationally cheap but does not capture synergistic or redundant document contributions (Nematov et al., 6 Jul 2025). For LLMs, stochastic generation mechanisms break strict Shapley axioms unless determinism is enforced or caching is used; various SHAP variants display tradeoffs among speed, principle satisfaction, and approximation fidelity (Naudot et al., 3 Nov 2025).

C. Time Series and High Dimensionality

For time series, segment-wise SHAP with equal-length segmentation and length-normalized attributions yields scalable and reliable explanations. The number of segments predominantly determines explanation quality, whereas fine-tuning the segmentation algorithm imparts marginal improvements (Serramazza et al., 3 Sep 2025).

D. Feature Removal and Safe Model Simplification

A widely used heuristic links small aggregate SHAP (or KernelSHAP) values to unimportant features. However, this is only justified under aggregation over the "extended" product-of-marginals distribution, not the empirical data. With this modification, vanishing aggregate SHAP guarantees that the feature can be safely removed with only an $O(d\sqrt\epsilon)$ change in prediction squared error over the extended support (Bhattacharjee et al., 29 Mar 2025).

5. Robustness, Statistical Guarantees, Limitations, and Failure Modes

A. Statistical Significance of Top-K Rankings

Monte Carlo SHAP estimates can be unstable due to sampling variability. Multiple hypothesis-testing frameworks, such as RankSHAP, use adaptive resampling and simultaneous confidence intervals to certify the stability of top-K SHAP feature rankings with high probability and dramatically reduce the required sample size compared to naive uniform allocation (Goldwasser et al., 28 Jan 2024).

B. WeightedSHAP and Optimal Utility

The uniform weighting over coalition sizes in classical SHAP may be suboptimal in settings where marginal contributions differ in informativeness or variance depending on coalition size. WeightedSHAP generalizes SHAP by learning data-driven weighting schemes to optimize a user-specified utility (e.g., prediction recovery accuracy), often improving upon the standard Shapley compromise (Kwon et al., 2022).

C. Impossibility Theorems and SHAP Limitations

No attribution scheme that is both complete (efficient) and linear, including SHAP and Integrated Gradients, can outperform random guessing for distinguishing local counterfactual model behaviors in sufficiently expressive model classes. SHAP collapses the effects of many locally distinct functions, making it unreliable for detecting spurious features or supporting algorithmic recourse except in trivially linear or infinitesimal perturbation regimes (Bilodeau et al., 2022).

D. Adversarial Manipulation and Label Leakage

Adversarial shuffling of model outputs (e.g., permuting outputs as a function of a protected feature) can "fool" the SHAP attributions. Exact SHAP is provably blind to these attacks, whereas KernelSHAP, Linear SHAP, and LIME may detect only high-intensity shuffles (Yuan et al., 12 Aug 2024). Similarly, class-dependent SHAP explanations may leak label information, artificially improving predicted class confidence when masking features. Distribution-aware SHAP variants (e.g., SHAP-KL, FastSHAP-KL) replace class-specific explanations with those based on KL-divergence to the full predictive distribution, mitigating leakage (Jethani et al., 2023).

E. Fingerprinting and Security

SHAP-based fingerprinting of attribution vectors enables detection of adversarial examples and robust anomaly detection in security contexts. When paired with unsupervised models (e.g., autoencoders), changes in attribution fingerprints under attack are strongly separable from clean data with high classification (F1, AUC) accuracy (Sharma et al., 9 Nov 2025).

6. Theoretical Advances and Open Problems

Recent advances provide unified frameworks relating SHAP computation to the tractability of expected value computations for simple (cardinality-based) power indices. SHAP is polynomially equivalent to expected-value computation under this regime, and interaction indices up to order $m$ can be reduced to $(n-m+1)(m+1)$ expectation evaluations and a polynomial-size linear system (Barceló et al., 4 Jan 2025).

A solvable Lie-algebraic structure of “value” operators mediates the invertibility properties of SHAP and justifies why aggregation over the product-of-marginals support is sound for safe feature removal (Bhattacharjee et al., 29 Mar 2025).

Open questions include the full characterization of power indices admitting constant-query computation, the development of optimally robust surrogates in adversarial and distributionally ambiguous settings, and extensions to chain-of-thought and higher-order interaction indices for complex modern models.

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References (by arXiv id):

• (Bitton et al., 2022) Latent SHAP: Toward Practical Human-Interpretable Explanations
• (Lundberg et al., 2017) Consistent feature attribution for tree ensembles
• (Chen et al., 3 Apr 2025) SHapley Estimated Explanation (SHEP)
• (Morales, 31 Oct 2025) SHAP values through General Fourier Representations: Theory and Applications
• (Cifuentes et al., 23 Jan 2024) The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
• (Wang et al., 7 Jun 2024) Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining LLM Predictions
• (Bilodeau et al., 2022) Impossibility Theorems for Feature Attribution
• (Ng et al., 31 Aug 2025) Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
• (Nematov et al., 6 Jul 2025) Source Attribution in Retrieval-Augmented Generation
• (Barceló et al., 4 Jan 2025) When is the Computation of a Feature Attribution Method Tractable?
• (Bhattacharjee et al., 29 Mar 2025) How to safely discard features based on aggregate SHAP values
• (Claborne et al., 30 Jul 2025) Consistency of Feature Attribution in Deep Learning Architectures for Multi-Omics
• (Goldwasser et al., 28 Jan 2024) Statistical Significance of Feature Importance Rankings
• (Kwon et al., 2022) WeightedSHAP: analyzing and improving Shapley based feature attributions
• (Serramazza et al., 3 Sep 2025) An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification
• (Jethani et al., 2023) Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
• (Yuan et al., 12 Aug 2024) Fooling SHAP with Output Shuffling Attacks
• (Naudot et al., 3 Nov 2025) llmSHAP: A Principled Approach to LLM Explainability
• (Sharma et al., 9 Nov 2025) Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting