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arxiv: 2604.04518 · v1 · submitted 2026-04-06 · 💻 cs.LG · cs.AI· cs.CV

Recognition: 2 theorem links

· Lean Theorem

Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them

Ole Delzer , Sidney Bender
Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:17 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CV
keywords spurious correlationsshortcut learningXAIcounterfactual knowledge distillationreproducibility studygroup imbalancedistributionally robust optimizationClever Hans effect
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The pith

XAI-based correction methods outperform non-XAI baselines for fixing spurious correlations in deep neural networks, especially Counterfactual Knowledge Distillation.

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

The paper unifies different research areas that all aim to prevent deep neural networks from using spurious correlations instead of true causal features. It does this by reproducing and comparing various correction methods on both synthetic and real datasets under tough conditions like few samples and imbalanced groups. XAI techniques turn out to work better overall than standard methods, with one called Counterfactual Knowledge Distillation performing most reliably at boosting generalization. The study also points out big practical problems: most methods need group labels that are expensive to get by hand, automatic tools for finding bad subgroups often fail on complex data, and there are too few minority examples to tune models properly. This is relevant for making AI safe in areas like medicine and driving.

Core claim

Through a comparative analysis, XAI-based methods generally outperform non-XAI approaches in improving generalization, with Counterfactual Knowledge Distillation proving most consistently effective. The practical application of many methods is hindered by dependency on group labels, as manual annotation is infeasible and automated tools like SpRAy struggle with complex features and severe imbalance. The scarcity of minority group samples in validation sets renders model selection and hyperparameter tuning unreliable.

What carries the argument

A unified comparative evaluation of XAI-based and non-XAI correction methods for addressing spurious correlations, shortcut learning, and group distributional non-robustness under limited data and subgroup imbalance.

Load-bearing premise

The evaluation assumes that methods can be fairly compared and that automated tools like SpRAy can identify relevant subgroups despite severe imbalance and complex features.

What would settle it

A replication experiment on the same synthetic and real-world datasets showing that a non-XAI baseline matches or exceeds the generalization performance of Counterfactual Knowledge Distillation would falsify the main result.

Figures

Figures reproduced from arXiv: 2604.04518 by Ole Delzer, Sidney Bender.

Figure 1
Figure 1. Figure 1: Research scope overview. A: Sketch illustrating how a confounder that is spuriously correlated with the class labels due to group imbalance can lead to Clever Hans behavior. Majority groups (green area) generalize well, while unseen minority group data (red area) is systematically misclassified. The few training samples in the minority group can easily be overfitted and hence do not prevent the classifier … view at source ↗
Figure 2
Figure 2. Figure 2: Selected samples from the CelebA dataset paired with their respective heatmaps as an overlay [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Result of a Spectral Relevance Analysis on Class ’0’ samples of the Colored MNIST dataset, [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sketch illustrating how A-ClArC and P-ClArC use a CAV to (a) add the confounder to or (b) [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Counterfactual Explanations generated for source class [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Conceptual illustration of dataset rebalancing using CFKD. (Left) An imbalanced training set [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Generalized depictions of (a) the symmetric and (b) the asymmetric dataset distributions in the [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Randomly selected samples of the Squares dataset grouped by their respective foreground and background intensities. Class A contains samples with low foreground intensity ([0.0, 0.5]) and Class B contains samples with high foreground intensity ([0.5, 1.0]). In the poisoned versions of this dataset, the background intensity acts as a Clever Hans feature. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Randomly selected samples of the CelebA dataset, where we added a watermark of varying opacity to each sample in order to artificially introduce a confounding feature. The samples are grouped by the two target classes, Smiling and Not Smiling, and by the opacity of their watermark (< 0.5 as transparent and ≥ 0.5 as opaque) 3.1.3 CelebA Blond Like CelebA Smiling, CelebA Blond is based on the original CelebA… view at source ↗
Figure 10
Figure 10. Figure 10: Randomly selected samples of the CelebA dataset, grouped by the two target classes [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Randomly selected samples of the CAMELYON17 dataset grouped by presence of cancer metas￾tases (classification target) and hospital of origin (confounder). Images from hospital A depict a pink color cast, while images from hospital B tend to be more purple. 3.1.5 Follicles The Follicles dataset consists of microscopic images of ovarian follicles and was originally introduced in Bender et al. (2023). The ta… view at source ↗
Figure 12
Figure 12. Figure 12: Randomly selected samples from the Follicles dataset, grouped by the two target classes Pri￾mordial (Class A) and Growing (Class B), and by the confounding feature, which is the size of the depicted follicle. Surrounding tissue is covered by a mask in order to help the model focus on the follicle. 3.2 Implementation details For the comparative analysis, we apply the XAI-based correction methods CFKD, P-Cl… view at source ↗
Figure 13
Figure 13. Figure 13: Decision boundaries and confidence regions of the uncorrected student models trained on (a) [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Examples of SpRAy clustering samples by unintended concepts. (a) In Camelyon17, SpRAy [PITH_FULL_IMAGE:figures/full_fig_p033_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: T-SNE visualizations of the Spectral Embedding for Class B of symmetric Squares. The circled [PITH_FULL_IMAGE:figures/full_fig_p034_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Comparison of (a) t-SNE and (b) UMAP visualizations of the Spectral Embedding for Class [PITH_FULL_IMAGE:figures/full_fig_p035_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: t-SNE visualizations of the spectral embeddings obtained by applying SpRAy to Class B samples [PITH_FULL_IMAGE:figures/full_fig_p036_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Decision boundaries and confidence regions for Squares after applying [PITH_FULL_IMAGE:figures/full_fig_p038_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Decision boundaries and confidence regions for Squares after applying [PITH_FULL_IMAGE:figures/full_fig_p040_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Impact of projection location l on validation and test performance when correcting with P-ClArC. Each data point shows the validation and test AGA for the optimal model at a given layer l, selected based on the highest validation AGA. The layer l indicates where the projection was inserted into the architecture, with l = 0 signifying a projection directly on the input image. The charts highlight a signifi… view at source ↗
Figure 21
Figure 21. Figure 21: Decision boundaries and confidence regions for Squares after applying [PITH_FULL_IMAGE:figures/full_fig_p043_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Comparison of the validation and test AGAs on symmetric Squares for the top 20 models, which [PITH_FULL_IMAGE:figures/full_fig_p045_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Comparison of the validation and test AGAs on symmetric CelebA Smiling for the top 20 models, [PITH_FULL_IMAGE:figures/full_fig_p046_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Comparison of the validation and test AGAs on Follicles for the top 20 models, which were [PITH_FULL_IMAGE:figures/full_fig_p046_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: The plots clearly show that RR-ClArC provides a more effective correction than the previously [PITH_FULL_IMAGE:figures/full_fig_p047_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: A selection of counterfactual explanations generated during the CFKD process for asymmetric [PITH_FULL_IMAGE:figures/full_fig_p048_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Decision boundaries and confidence regions for Squares after applying [PITH_FULL_IMAGE:figures/full_fig_p049_27.png] view at source ↗
read the original abstract

Deep Neural Networks (DNNs) are increasingly utilized in high-stakes domains like medical diagnostics and autonomous driving where model reliability is critical. However, the research landscape for ensuring this reliability is terminologically fractured across communities that pursue the same goal of ensuring models rely on causally relevant features rather than confounding signals. While frameworks such as distributionally robust optimization (DRO), invariant risk minimization (IRM), shortcut learning, simplicity bias, and the Clever Hans effect all address model failure due to spurious correlations, researchers typically only reference work within their own domains. This reproducibility study unifies these perspectives through a comparative analysis of correction methods under challenging constraints like limited data availability and severe subgroup imbalance. We evaluate recently proposed correction methods based on explainable artificial intelligence (XAI) techniques alongside popular non-XAI baselines using both synthetic and real-world datasets. Findings show that XAI-based methods generally outperform non-XAI approaches, with Counterfactual Knowledge Distillation (CFKD) proving most consistently effective at improving generalization. Our experiments also reveal that the practical application of many methods is hindered by a dependency on group labels, as manual annotation is often infeasible and automated tools like Spectral Relevance Analysis (SpRAy) struggle with complex features and severe imbalance. Furthermore, the scarcity of minority group samples in validation sets renders model selection and hyperparameter tuning unreliable, posing a significant obstacle to the deployment of robust and trustworthy models in safety-critical areas.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. This reproducibility study unifies fragmented research on spurious correlations, shortcut learning, Clever Hans effects, DRO, and IRM by comparing XAI-based correction methods (e.g., Counterfactual Knowledge Distillation) against non-XAI baselines. Using synthetic and real-world datasets under limited data and severe subgroup imbalance, it claims XAI methods generally outperform non-XAI approaches, with CFKD most consistently effective at improving generalization, while also documenting practical obstacles from group-label dependency and unreliable automated subgroup detection via tools like SpRAy.

Significance. If the reported superiority of XAI methods holds under reliable subgroup recovery, the work would usefully consolidate cross-community efforts on model robustness and supply empirical guidance for deploying trustworthy DNNs in safety-critical settings.

major comments (2)
  1. [Abstract] Abstract: The headline claim that XAI-based methods (particularly CFKD) outperform non-XAI baselines rests on generalization metrics computed over subgroups identified by automated tools such as SpRAy. The abstract itself states that these tools 'struggle with complex features and severe imbalance'—precisely the experimental regime—yet the comparative evaluation proceeds without additional controls (e.g., sensitivity analysis or oracle-group experiments) to show that performance differences are not artifacts of the same noisy subgroup signal applied uniformly to all methods.
  2. [Evaluation / Experiments] Evaluation / Experiments section: No error bars, exact metric definitions, statistical significance tests, or details on hyperparameter tuning under severe class/subgroup imbalance are reported. This absence is load-bearing because the central empirical ranking (XAI > non-XAI, CFKD best) cannot be assessed for robustness when minority-group validation samples are scarce, as the abstract itself notes.
minor comments (1)
  1. [Abstract] The abstract could more explicitly list the concrete datasets, the precise definition of 'generalization improvement,' and the number of runs used for each comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our reproducibility study. We address the major comments point by point below, with proposed revisions to improve the manuscript's robustness and transparency.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that XAI-based methods (particularly CFKD) outperform non-XAI baselines rests on generalization metrics computed over subgroups identified by automated tools such as SpRAy. The abstract itself states that these tools 'struggle with complex features and severe imbalance'—precisely the experimental regime—yet the comparative evaluation proceeds without additional controls (e.g., sensitivity analysis or oracle-group experiments) to show that performance differences are not artifacts of the same noisy subgroup signal applied uniformly to all methods.

    Authors: We appreciate this observation. The study deliberately evaluates methods under realistic conditions where group labels are unavailable, using automated subgroup detection (SpRAy) as employed in the reproduced works. The abstract explicitly flags the tools' limitations to frame the results as practical rather than ideal. Because every method receives the identical noisy subgroup signal, observed differences reflect how each correction approach interacts with imperfect detection. To strengthen the claim, we will add a sensitivity analysis varying SpRAy hyperparameters and include oracle-group experiments on the synthetic datasets (where ground-truth labels exist) to isolate whether XAI advantages persist under perfect subgroup information. These elements will be incorporated in the revised manuscript. revision: partial

  2. Referee: [Evaluation / Experiments] Evaluation / Experiments section: No error bars, exact metric definitions, statistical significance tests, or details on hyperparameter tuning under severe class/subgroup imbalance are reported. This absence is load-bearing because the central empirical ranking (XAI > non-XAI, CFKD best) cannot be assessed for robustness when minority-group validation samples are scarce, as the abstract itself notes.

    Authors: We agree that these details are necessary for rigorous assessment, particularly given the acknowledged scarcity of minority-group samples. In the revised manuscript we will: (i) report error bars as standard deviations across multiple random seeds; (ii) provide exact definitions for all metrics (e.g., worst-group accuracy, average accuracy); (iii) include statistical significance tests (paired t-tests or Wilcoxon tests) comparing method rankings; and (iv) document the hyperparameter search procedure, including how imbalance was handled during tuning (e.g., stratified sampling or majority-group validation with post-hoc checks). These additions will allow readers to evaluate the stability of the reported ranking. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical reproducibility study with external benchmarks

full rationale

The paper performs a comparative empirical evaluation of XAI and non-XAI correction methods on synthetic and real-world datasets, reporting generalization improvements without any claimed mathematical derivation, self-definitional parameters, or predictions that reduce to fitted inputs by construction. Subgroup identification via SpRAy is presented as a practical limitation rather than a load-bearing definitional step, and all results are benchmarked against standard external datasets and baselines. No self-citation chains or ansatzes are invoked to force the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The study relies on standard machine learning evaluation assumptions such as the existence of identifiable subgroups and the validity of generalization metrics under imbalance; no new axioms or invented entities are introduced.

axioms (2)
  • domain assumption Subgroup labels or proxies are necessary for applying most correction methods
    Abstract states that practical application is hindered by dependency on group labels, assuming such labels define the spurious correlation problem.
  • domain assumption Automated tools like SpRAy can identify relevant features despite complex data
    Mentioned as struggling with complex features and imbalance, yet used as baseline.

pith-pipeline@v0.9.0 · 5572 in / 1383 out tokens · 25872 ms · 2026-05-10T20:17:40.238163+00:00 · methodology

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · 2 internal anchors

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