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REVIEW 3 major objections 5 minor 71 references

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Explainable AI can show exactly why synthetic tabular data look wrong.

desk verdict A genuinely useful diagnostic toolkit for synthetic tabular data, but the headline claim that XAI reveals generator flaws rather than detector artifacts is asserted, not yet demonstrated. read the letter →

arxiv 2504.20687 v1 pith:SX7TSS2C submitted 2025-04-29 cs.LG stat.ML

Jan Kapar , Niklas Koenen , Martin Jullum This is my paper · ORCID
classification cs.LGstat.ML
keywords syntheticdataqualitygenerativeartificialintelligenceexplainableinterpretablemachinelearningdetectiontabularShapleyvaluescounterfactualexplanations
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

Synthetic tabular data are usually graded with aggregate scores—statistical distances, utility measures, and detection accuracy—that can disagree and never say where generation failed. This paper proposes training a binary classifier to distinguish real from synthetic rows and then using explainable-AI tools on that classifier: permutation feature importance, partial dependence and ICE plots, Shapley values, and counterfactual explanations. The result is a feature-level audit that localizes unrealistic values, broken feature dependencies, and under- or over-represented regions of the data distribution. Demonstrations on two tabular datasets show the approach surfacing weaknesses that conventional quality metrics overlook.

What carries the argument

The load-bearing object is the detection classifier, a binary model $C$ mapping each row to its estimated probability of being real, trained on labeled real and synthetic data. It converts the unsupervised problem of judging a generative model into a supervised classification problem that XAI methods can address. Four families of explanations carry the argument: permutation feature importance and global Shapley values identify the features and interactions that separate real from synthetic; partial dependence and individual conditional expectation plots locate value regions where the classifier confidently flags synthetic rows; marginal and conditional Shapley values decompose individual predictions, with the conditional version respecting feature dependencies; and counterfactual explanations compute minimal, plausible feature changes that flip a synthetic row's classification to real. The qualitative distinction between marginal ('true to the model') and conditional ('true to the data') explanations is what lets the authors attribute a detected flaw to the generator rather than to the classifier's independence assumptions.

What would settle it

Run a controlled experiment with an injected flaw: take a real tabular dataset, fit a generator, then deliberately break one known property—for example, randomizing one feature's values or severing one documented dependency—in a new synthetic sample. If the detection-plus-XAI pipeline does not rank that injected flaw at the top of feature importance and does not show a clear anomaly in the corresponding effect plot or Shapley decomposition, the claim that these tools reveal generator weaknesses is refuted.

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Extended reading notes

Core claim

The central claim is that a well-trained real-versus-synthetic detection classifier is not just a quality score: it is a supervised proxy whose explanations reveal why synthetic data are distinguishable. On the paper's own terms, the classifier answers four questions: which features and dependencies were hardest for the generator, how the generator behaves in low- and high-density regions, which features make individual synthetic or real rows look wrong, and which minimal changes would make a detected synthetic row look real. The authors show that global tools (permutation feature importance, Shapley feature and interaction importance, partial dependence and ICE plots) expose weak spots such as non-integer values in an integer feature, rare classes that are under-represented, and interactions that the generator does not retain. Local Shapley analysis further shows that a synthetic row can fail because of an implausible combination of individually plausible values, for instance a very young person with a school-leaving age, and counterfactual edits show the small changes needed to make that row pass as real. Conventional metrics, they argue, would report only that the synthetic data differ, not where or why.

Load-bearing premise

The whole diagnostic chain rests on one premise: the detection classifier is a trustworthy proxy for synthetic data quality, so a low accuracy means either a weak classifier or genuinely good synthetic data and the explanations reflect generator flaws rather than classifier artifacts.

Editorial extensions

If this is right

  • Feature-importance rankings become a targeted debugging list: a generator's weak spots are the features and interactions the detector leans on most.
  • Partial-dependence and ICE plots turn quality assessment into a density-coverage map, showing which real-data regions the synthetic data over- or under-represent.
  • Local Shapley decompositions identify the specific value combinations that make an individual synthetic row look implausible, even when each feature alone looks fine.
  • Counterfactual edits give an actionable repair: for a flagged synthetic row, the minimal changes that make it pass as real indicate what the generator got wrong.
  • The same workflow is transferable across generators and datasets, so a fixed audit pipeline can compare two synthesizers or monitor one as it is modified.

Reading between the lines

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

  • A natural extension is to train the detector on real data the generator never saw, turning the same diagnostics into a privacy or generalization audit; the paper mentions this as future work.
  • The diagnostic plots could serve as regression tests in generator development: after a change, a regenerate-and-compare step would check whether the flagged PDP dip or interaction importance disappears.
  • Implicit in the worked examples is a practical rule: when features are locally dependent, prefer conditional Shapley values over marginal ones, because marginal attributions can blame the wrong feature for an unrealistic combination.
  • The counterfactual output could be reused as a data-cleaning prescription for individual synthetic rows, not only as a diagnosis of the generator.
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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

3 major / 5 minor

Summary. The paper proposes an evaluation framework for synthetic tabular data in which a binary classifier is trained to distinguish real from synthetic observations, and explainable AI methods are then applied to that classifier to explain why the synthetic data are distinguishable. The authors map four XAI tools to four diagnostic questions: permutation feature importance and Shapley values for challenging features and dependencies (Q1), partial dependence and ICE curves for unrealistic regions and over/underrepresented areas (Q2), Shapley decompositions for individual real and synthetic instances (Q3), and counterfactual explanations for minimal changes that would make a synthetic instance look real (Q4). The approach is demonstrated on the Adult dataset with TabSyn and, in an appendix, on the Nursery dataset with CTGAN. The central claim is that this XAI-on-detector pipeline provides deeper insight than conventional synthetic-data quality metrics and uncovers issues that standard evaluation techniques overlook.

Significance. If the central claim were fully established, the framework would be a practically useful addition to the synthetic-data auditing toolbox, because it moves beyond scalar quality scores toward feature- and instance-level diagnoses. The paper has real strengths: it gives a clean mapping from diagnostic questions to specific XAI tools, it uses a sensible detection model (tuned XGBoost) and compares several classifier families, it discusses the important distinction between marginal and conditional explanations, and it ships a public repository with scripts for tuning, training, and reproduction. The case studies are internally coherent and the XAI computations follow standard practice. However, the headline contribution is an empirical claim about explanatory depth relative to conventional metrics, and that claim is not yet backed by the experiments as written.

major comments (3)
  1. [Abstract; Section 5] The abstract and Section 5 state that the approach 'uncovers issues overlooked by standard evaluation techniques,' but the experiments contain no comparison to conventional quality metrics such as maximum mean discrepancy, Wasserstein distance, precision/recall, or utility-based measures. Section 5.1 compares detection classifiers against each other, not against standard evaluation metrics, and Sections 5.2 and Appendix A present only the XAI-based diagnostics. Without a side-by-side demonstration that standard metrics fail to flag the education_num non-integer pattern, the occupation rare-class issue, or the class-dependency issue in Nursery, the claimed advantage over standard evaluation techniques is not demonstrated. I request an experiment that computes at least one or two standard fidelity/utility metrics on the same synthetic datasets and explicitly compares what those metrics indicate with what the XAI diagnostics indicate.
  2. [Section 6; Section 4.2.2] The pipeline attributes detector explanations to flaws of the generative model, but this attribution is assumed rather than validated. Section 6 itself concedes that the detector 'can be too sensitive and might detect deviations such as different numeric precision which might not be relevant to assess the practical utility of the synthetic data.' The Adult example interprets the periodic PDP dips for education_num as evidence that TabSyn generates non-integer values; this is plausible, but nothing in the experiments verifies that the highlighted pattern is a generator flaw rather than a detector artifact. The paper would be substantially strengthened by a validation study, for example by injecting controlled flaws into a generator and checking whether the XAI diagnostics recover them, or by repairing a diagnosed issue (e.g., rounding education_num to integers) and showing that a conventional fidelity or utility metric improves. Without such validation, 'what is wrong with the synthetic data' is operationally only 'what the chosen detector exploits.'
  3. [Section 4.2.1; Section 5.2.1] The feature-level conclusions are based on a single detector family (XGBoost), but PFI and TreeSHAP are known to reflect the model's internal structure as well as the data distribution. Section 5.1 shows that XGBoost has higher detection accuracy than logistic regression and random forest, but it does not examine whether the explanations are stable across detector choices. Since the central claim is that the XAI diagnostics reveal generator weaknesses rather than detector artifacts, I ask for a sensitivity analysis in which at least the global importance ranking and the PDP for education_num are recomputed with a different high-performing detector (e.g., a tuned random forest or a regularized logistic regression with engineered features) and the agreement or disagreement of the diagnostic conclusions is reported.
minor comments (5)
  1. [Figure 3b] The class label 'unknwon' on the y-axis appears to be a typo for 'unknown.'
  2. [Section 5.2.4] The text reads '5× 105 Monte Carlo samples'; this should presumably be 5×10^5 (or 500,000) Monte Carlo samples.
  3. [Figures 2b and 8b] The x-axis label 'degree 1 2' is ambiguous; it would be clearer to write 'Degree 1' and 'Degree 2' as separate legend entries or to explain the degrees in the caption.
  4. [Section 5.2.3] The conditional Shapley values are estimated with conditional inference trees, but the paper does not state the hyperparameter settings or the number of trees used for this estimator; a brief reproducibility note would be useful.
  5. [Section 5.2.2] The interpretation of the ICE curves as indicating 'low fidelity but high diversity' for education_num is somewhat compressed; since this is a key illustrative conclusion, one or two sentences explaining how the relative shares of real and synthetic ICE curves below/above 0.5 map to fidelity and diversity would improve readability.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the proposed XAI-on-detector evaluation is self-contained, and the acknowledged detector-dependence and use of generator-based XAI tools are stated limitations rather than reductions.

full rationale

The paper's central method is to train a binary detector to separate real and synthetic data and then explain its predictions with PFI, PDP/ICE, Shapley values, and counterfactuals. This is an empirical demonstration on two datasets, not a derivation that reduces to its own inputs by construction: no fitted parameter is renamed as a prediction, no uniqueness theorem from the authors' prior work is invoked to force a choice, and no equation equates the XAI output with the claimed conclusion. The only author-overlap citations are to externally published methods: conditional sampling in [17], MCCE counterfactuals in [21], and conditional inference trees in [71]. These are used as tools with published code and peer review, and they are not the object being evaluated. The paper explicitly flags its real dependency in Section 6: it 'depends on the performance of a binary classifier used for synthetic data detection,' and 'some of the XAI methods, especially conditional approaches and counterfactuals generators, are even based on generative modeling themselves.' Those are honest limitations about validity and sensitivity, not circular reductions. The lack of ground-truth validation that a highlighted detector artifact corresponds to an actual generator flaw is a correctness risk, not circularity, because the paper never equates its explanations with a fitted outcome by construction. The low score reflects only the presence of minor non-load-bearing self-citations to methods whose authors overlap with the present paper.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No new entities are introduced. The method relies on standard XAI and tree models; the central claim rests on the detector-as-proxy assumption and on the accuracy of the XAI approximations.

assumptions (3)
  • domain assumption A well-tuned binary classifier trained to distinguish real from synthetic rows provides a valid measure of synthetic data quality.
    Section 4.1; the paper notes in the Discussion that the approach depends on the classifier's performance, but the methodology relies on this proxy assumption.
  • domain assumption XAI explanations of the detector reflect properties of the generative model rather than artifacts of the detector itself.
    Section 4.2 and Section 6; the interpretive leap from model explanations to generator deficiencies is central and is only partially validated.
  • domain assumption The XAI estimators used are accurate enough for the qualitative conclusions.
    KernelSHAP with 2000 coalitions, conditional inference trees, and MCCE with 5e5 Monte Carlo samples are approximations whose estimation error is not quantified (Sections 5.2.3 and 5.2.4).

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Cite this review

Pith. "Pith review of What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models." pith.science (2026). https://pith.science/paper/SX7TSS2C

@misc{pith2026250420687,
  author       = {Pith},
  title        = {Pith review of: What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SX7TSS2C}},
  note         = {Machine review of arXiv:2504.20687}
}
read the original abstract

Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from statistical distances to predictive performance, often providing conflicting results. Moreover, they fail to explain or pinpoint the specific weaknesses in the synthetic data. To address this, we apply explainable AI (XAI) techniques to a binary detection classifier trained to distinguish real from synthetic data. While the classifier identifies distributional differences, XAI concepts such as feature importance and feature effects, analyzed through methods like permutation feature importance, partial dependence plots, Shapley values and counterfactual explanations, reveal why synthetic data are distinguishable, highlighting inconsistencies, unrealistic dependencies, or missing patterns. This interpretability increases transparency in synthetic data evaluation and provides deeper insights beyond conventional metrics, helping diagnose and improve synthetic data quality. We apply our approach to two tabular datasets and generative models, showing that it uncovers issues overlooked by standard evaluation techniques.

Figures

Figures reproduced from arXiv: 2504.20687 by the authors.

Figure 1
Figure 1. (a) Synthetic data detection performance of logistic regression, random forest [69] and XGBoost models [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Feature importance values for synthetic data detection with XGBoost for ten TabSyn-generated synthetic [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. ICE/PDP for synthetic data detection with XGBoost for TabSyn-generated synthetic adult data. (a) Numeric [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Force plots for conditional and marginal Shapley values decomposing the XGBoost prediction for an exem [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Waterfall plots for Shapley interaction values decomposing the XGBoost prediction for an examplary [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Force and waterfall plots for, respectively, (a) conditional Shapley values and (b) Shapley interaction values [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Four counterfactual explanations for an examplary instance of TabSyn-generated synthetic adult data. High [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Feature importance values for synthetic data detection with XGBoost for ten CTGAN-generated synthetic [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: ICE/PDP for synthetic data detection with XGBoost for CTGAN-generated synthetic nursery data. (a) [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Waterfall plots for Shapley interaction values decomposing the XGBoost prediction for an examplary [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Four counterfactual explanations for an examplary instance of CTGAN-generated synthetic nursery data. [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.