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REVIEW 4 major objections 5 minor 43 references

A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that unifying SHAP with counterfactual necessity/sufficiency metrics makes interpretable MCI and Alzheimer's diagnoses robust enough for clinical decision support.

desk verdict First real application of Mothilal's necessity/sufficiency metrics to MCI/AD, honest about its limits, but the robustness claim in the framing is stronger than the evidence supports. read the letter →

arxiv 2412.09376 v2 pith:FTSQIXEJ submitted 2024-12-12 cs.LG

Maria Eleftheria Vlontzou , Maria Athanasiou , Kalliopi Dalakleidi , Ioanna Skampardoni , Christos Davatzikos , Konstantina Nikita This is my paper · ORCID
classification cs.LG
keywords interpretablemachinelearningAlzheimer'sdiseasediagnosismildcognitiveimpairmentSHAPcounterfactualexplanationsnecessityandsufficiencyensembleMRIvolumetricfeatures
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to make machine-learning diagnoses of mild cognitive impairment (MCI) and Alzheimer's disease (AD) trustworthy by pairing high-accuracy classification with explanations that are checked for robustness. Using MRI-derived brain-region volumes and genetic markers from a public cohort of 1,463 subjects, it trains six classifiers under a bagging one-versus-one decomposition; the best model reaches 87.5% balanced accuracy and a 90.8% F1-score. The central move is to unify SHAP attributions with counterfactual explanations through necessity and sufficiency metrics, so that a feature counts as important only if changing it (or fixing it) demonstrably changes (or preserves) the model's output. The authors argue that the resulting explanations—highlighting entorhinal areas, lateral ventricles, temporal gyri, and the ApoE-linked SNP rs429358—are consistent with known Alzheimer's neuropathology. If right, this gives clinicians a decision-support tool that is both accurate and transparent about why a patient was classified as MCI or AD.

What carries the argument

The central machinery is the unification of feature attribution with counterfactual explanations through the actual-causality notions of necessity and sufficiency. Necessity is computed by generating counterfactuals that change only the feature under test and measuring the fraction that produce a valid class flip; sufficiency fixes that feature and permutes all others, measuring one minus the fraction of valid counterfactuals produced. These two scalar metrics turn a ranked feature list from SHAP into statements about causal indispensability and causal adequacy, and the paper applies them to the top-10 SHAP features of the MCI-versus-AD classifier.

What would settle it

Recompute DiCE necessity and sufficiency for the top-10 SHAP features on the same MCI-versus-AD data and compare them with ten randomly selected features; if the random features achieve comparable or higher scores, the unification does not actually show that SHAP's top features are special, and the robustness claim fails.

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Extended reading notes

Core claim

The paper's central claim is that a single framework can deliver diagnostically competitive and robustly interpretable predictions for MCI and AD by decomposing the three-class problem into one-versus-one binary subproblems, resampling the majority MCI class via bagging, and combining attribution-based methods (SHAP, Gini, LIME, PDP) with counterfactual-based methods (Permute Attack, DiCE) inside a unification scheme grounded in actual causality. The unification computes necessity and sufficiency scores for the top-10 SHAP features, quantifying whether a feature is indispensable for the outcome and whether fixing it alone can guarantee the outcome. The authors show that individual top features have low necessity in a 208-feature space, but the combined top-10 features achieve a necessity of 29.8% with DiCE, nearly half the 63.7% necessity of the remaining 95% of features, with sufficiency scores of 33–38%; they interpret this as evidence that the SHAP ranking captures features that genuinely drive the model.

Load-bearing premise

The load-bearing premise is that the counterfactual generators can produce valid, diverse alternative outcomes when individual features are changed; if they cannot, the necessity and sufficiency scores do not measure what they claim, and the central claim of robust explanations collapses.

Editorial extensions

If this is right

  • Explanation robustness becomes a measurable quantity: a model whose top features score low on necessity and sufficiency can be flagged as untrustworthy before clinical adoption.
  • Combining MRI volumes with SNPs in one model shows that both anatomical (hippocampus, entorhinal cortex, ventricles) and genetic (rs429358/APOE) factors drive AD risk, supporting multimodal diagnostic inputs.
  • The bagging with one-versus-one decomposition offers a template for other imbalanced multiclass medical datasets where the majority class dominates but every class matters.
  • The finding that combined top-10 features have much higher necessity than any single feature suggests explanations should be read as feature sets rather than individual biomarkers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the necessity/sufficiency framework could be turned into a model- or feature-selection criterion—choose the classifier and feature set that maximize combined-top-feature necessity—rather than applying the unification only post hoc to a fixed model.
  • Inference: because one counterfactual generator (Permute Attack) produced zero necessity for every single feature, the choice of generator is not neutral; a fair robustness assessment should require multiple generators or validate counterfactual quality before scoring necessity.
  • Inference: the same unification could be applied to longitudinal data to test whether features necessary for conversion from MCI to AD differ from features necessary for cross-sectional diagnosis, a question the present cross-sectional design cannot answer.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a comprehensive interpretable machine learning framework for MCI and AD diagnosis using ADNI data, combining bagging with one-versus-one classification to handle multiclass imbalance, evaluating six classifiers under nested cross-validation, and applying SHAP, LIME, PDP, and counterfactual methods (Permute Attack and DiCE). The best classifier (SVM) achieves 87.5% balanced accuracy and 90.8% F1-score. A unification approach based on Mothilal et al. computes necessity and sufficiency of top-10 SHAP features to assess robustness of interpretations. The paper reports that Permute Attack yields zero necessity for individual top features while DiCE yields low values, and it discusses the method-dependence of these metrics. The main claim is that the framework provides robust interpretable predictions of MCI/AD diagnosis.

Significance. If the central claim holds, the paper would make a useful contribution to interpretable ML for dementia diagnosis: it applies a relatively recent unification framework (Mothilal et al.) to a clinically relevant problem, combines volumetric MRI and SNP features, reports results on a public dataset, and makes code available. The strengths include nested cross-validation for hyperparameter tuning and performance evaluation, explicit comparison with prior multiclass ADNI studies, and a multi-method XAI analysis whose qualitative findings (e.g., hippocampus, entorhinal cortex, lateral ventricles, APOE-related rs429358) align with established AD neuropathology. However, the paper's central claim about the robustness of the interpretations rests on the necessity/sufficiency analysis, and that analysis is method-dependent in a way the paper itself concedes; without sensitivity checks or baseline comparisons, the unification does not yet demonstrate robustness of SHAP attributions.

major comments (4)
  1. [Results: Unified explanations, Fig. 6, Discussion] The central claim that the framework assesses the robustness of interpretability methods is not supported by the necessity/sufficiency results as presented. Fig. 6 shows that Permute Attack yields necessity 0 for every individual top-10 feature and for the top-10 combined, while DiCE yields individual necessity values of only 1% to 9%; the Discussion concedes that the calculation of necessity and sufficiency is "highly dependent on the selected counterfactual generation method" and that low necessity is expected with a large number of features. These results show that individual features are largely redundant in a 208-feature space, but they do not show that the SHAP attributions are robust. The authors should add a sensitivity analysis across counterfactual generation settings, a random-feature baseline, or validation on data with known causal structure before claiming that the unification "assesses the interpretability techniques' robustness" (Abstract) or that the results "further showcase their significance" (Abstract).
  2. [Table 1 and Classification space] The headline performance (87.5% balanced accuracy, 90.8% F1) is the best of six classifiers selected after nested CV, yet the paper does not correct for multiple comparisons or for selecting the maximum over classifiers. The reported t-test comparisons are pairwise without adjustment for the multiple tests, and the MLP is the only classifier found significantly worse. Since the SVM is selected as the best model and then used for all interpretability analyses, the authors should justify that the selection does not inflate the reported performance, or present performance with appropriate correction, e.g., reporting all classifiers' cross-validated performance with confidence intervals and a multiple-comparison correction.
  3. [Methods: Unifying feature attribution-based methods and counterfactual explanations, Eqs. (1)-(2)] The necessity and sufficiency metrics in Eqs. (1) and (2) depend critically on the ability of the counterfactual generator to produce valid counterfactuals under the allowed perturbations, but the paper does not report the fraction of instances for which valid counterfactuals were successfully generated for each method and each feature subset. Without this information, a zero necessity value could reflect generation failure rather than true non-necessity, and the sufficiency values could be artificially high or low depending on the generator's success rate. The authors should report generation success rates and, if they are not near 100%, discuss how this affects the interpretation of Figs. 6a-d.
  4. [Methods: Overview of the conceptual framework] The term "robust interpretable predictions" is used in the Abstract and the Methods Overview, but the paper does not define what robustness means in this context or how the unification framework measures robustness beyond the necessity/sufficiency metrics. The paper should either provide a precise definition of interpretation robustness and a justification that the necessity/sufficiency metrics capture it, or temper the claim to avoid overstating the evidence. As it stands, the claim that the unification "assesses the interpretability techniques' robustness" is an over-reading of the results, given the method-dependence acknowledged in the Discussion.
minor comments (5)
  1. [Results, Fig. 2 caption] The caption reads "the Mild Cognitive Impairment (MCI) classes" in one place; it should read "MCI class" to be consistent with the binary subproblem framing.
  2. [Methods, Eqs. (1)-(2)] The notation "x31(CFi)" appears corrupted and should be a proper indicator function, e.g., 1[CF_i is valid]. Please fix the typesetting and define all symbols.
  3. [Results, Table 1] The table caption states that the optimal metrics are highlighted in bold, but the bold entries do not clearly correspond to a single stated selection rule (mean CV, max CV, or TTS). Please clarify which column is used for model selection and mark it explicitly.
  4. [Methods: Classification space] The description of the bagging strategy says "two different training subsets were created, both containing all CN and AD training samples and each of them a different half of the majority class samples." It is unclear whether the two halves are disjoint and how they are sampled; please specify the exact procedure and whether the CN and AD samples are also split or kept intact.
  5. [Results: Explanations from individual interpretability methods] The text mentions that LIME explanations were obtained for True Positive, True Negative, False Positive, and False Negative instances, but only one LIME example is shown in Fig. 3. Please state where the other examples are presented (or note that they appear in the Supplementary material).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical evaluation with held-out data and externally sourced XAI methods.

full rationale

The paper is an empirical fitting-and-evaluation study rather than a derivation chain. Classifiers are trained on an 80:20 split with nested cross-validation and evaluated on held-out data, so the reported balanced accuracy and F1-score are independent of the training procedure. The necessity/sufficiency unification is imported from the external work of Mothilal et al. (ref. 18) and is applied post hoc to SHAP-selected features; it does not feed back into classifier training or performance evaluation, nor is any fitted parameter later relabeled as a prediction. The only self-citation is ref. 40, which supports the general usefulness of SHAP-style feature ranking alongside an external citation (ref. 6), so it is not load-bearing. The paper's own concession that necessity/sufficiency results are highly dependent on the chosen counterfactual generation method is a validity and robustness limitation, not a circular argument, because no claim reduces by construction to its own inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest on several domain assumptions about the ADNI dataset, the covariate adjustment procedure, the validity of the adopted necessity/sufficiency framework, and the reliability of SHAP approximations in a high-dimensional feature space. The free parameters are mostly analysis choices (hyperparameters, top-10 subset, number of counterfactuals) that affect the quantitative results but are not hidden constants. No new entities (particles, forces, dimensions) are introduced.

free parameters (4)
  • Classifier hyperparameters (e.g., SVM C, kernel parameters, RF depth) = tuned via 5x4 nested CV, exact values not reported
    The reported performance (87.5% balanced accuracy, 90.8% F1) depends on the hyperparameter selection; without the exact values and search space, independent replication is harder.
  • Number of counterfactuals per instance (nCF) = 4 (presented results; averaged over 1,2,4,8)
    The necessity and sufficiency metrics are averaged over 1,2,4,8 counterfactuals per instance, and the presented results use 4. This choice can affect the computed metrics.
  • Top-10 SHAP feature subset size = 10
    The necessity/sufficiency analysis is performed only on the top-10 features identified by SHAP. The choice of 10 is arbitrary and directly affects the combined necessity/sufficiency values.
  • MCI majority-class split into two fractions = 2
    Bagging splits the majority MCI class into two fractions, each paired with all CN and AD samples. This design choice addresses imbalance but reduces MCI diversity in each bag and may affect the learned decision boundaries.
assumptions (5)
  • domain assumption The ADNI dataset of 1463 baseline subjects is representative of the target clinical population for MCI/AD diagnosis.
    The framework is trained and evaluated entirely on ADNI data; generalization to other cohorts is explicitly left to future work (Discussion).
  • domain assumption Linear covariate residualization using healthy controls removes age, sex, and brain volume effects without removing disease-related signal.
    In Methods, Dataset, a linear regression on 449 healthy controls is used to residualize ROI volumes. If disease-related atrophy is correlated with these covariates, the residualization could distort disease features.
  • domain assumption The Mothilal et al. (2021) definitions of necessity and sufficiency are valid and meaningful measures of feature importance and interpretation robustness.
    The unify method relies entirely on these definitions, taken from reference 18, without independent derivation or validation in this clinical setting.
  • domain assumption SHAP values computed for the SVM classifier accurately approximate true Shapley values.
    Model-agnostic SHAP (KernelSHAP) is applied to a 208-feature SVM; approximation quality can degrade in high dimensions, and this is not assessed.
  • domain assumption The 54 selected SNPs are the relevant AD-related genetic variants and their coding (0, 0.5, 1) as additive allele counts is correct.
    The SNP set is described as 'AD related' and includes known variants like APOE, but the criteria for selecting these 54 SNPs from the larger ADNI genetic data are not detailed.

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Pith. "Pith review of A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis." pith.science (2026). https://pith.science/paper/FTSQIXEJ

@misc{pith2026241209376,
  author       = {Pith},
  title        = {Pith review of: A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FTSQIXEJ}},
  note         = {Machine review of arXiv:2412.09376}
}
read the original abstract

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer's Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques' robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features' necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.

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