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On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models

T0 review · 1 major / 1 minor · reviewed 2026-05-23 · grok-4.3

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Pith's one-line read Conditional independence tests true for all distributions rarely catch errors in graphical models, unlike those implied only by the graph.

desk verdict The paper cleanly separates universal CIs from graph-specific ones and shows why only the latter can flag model errors, but offers no evidence this distinction survives finite-sample noise. read the letter →

arxiv 2502.08531 v3 submitted 2025-02-12 cs.LG stat.ML

Philipp M. Faller , Dominik Janzing This is my paper · ORCID
classification cs.LGstat.ML
keywords conditionalindependencegraphicalmodelsredundancyerrordetectionstructurelearningtestscausaldiscovery
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 examines redundant conditional independence tests that remain after a graphical model is learned from data. These unused tests can sometimes detect or correct mistakes introduced by unreliable statistical tests during discovery. The authors separate two kinds of redundancy: statements that must hold no matter what the probability distribution is, and statements that hold only because of the particular graph structure chosen. Only the second kind supplies extra information that can reveal or fix errors. The distinction matters because real datasets produce noisy test results, so algorithms need ways to use leftover checks selectively rather than uniformly.

What carries the argument

The distinction between universal conditional (in)dependencies (true for every distribution) and graph-specific ones (implied solely by the learned structure) when evaluating which redundant tests can expose errors.

What would settle it

Generate data from a known graph, inject controlled errors into the independence tests used by a discovery algorithm, then measure whether graph-specific redundant tests flag or repair those errors more reliably than universal ones.

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Extended reading notes

Core claim

Conditional (in)dependence statements that hold for every probability distribution are unlikely to detect and correct errors in the learned model, in contrast to those that follow only from graphical assumptions.

Load-bearing premise

The theoretical split between universal and graph-specific independencies produces measurable differences in error detection on finite noisy data.

Editorial extensions

If this is right

  • Redundant tests that follow from graphical assumptions can be used to detect or sometimes correct errors produced by faulty statistical tests.
  • Redundant tests that hold for every probability distribution should not be expected to detect or correct such errors.
  • Discovery algorithms must choose which redundant tests to apply according to whether they are graph-specific or universal.
  • The reliability of conditional-independence-based methods improves only when graph-specific redundancies are consulted after initial model construction.

Reading between the lines

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

  • Algorithms could be redesigned to reserve graph-specific independence queries for post-learning verification rather than using them only during search.
  • The same distinction may apply to other structure-learning procedures that leave some independence relations untested during model construction.
  • Empirical studies on benchmark datasets with known ground-truth graphs could quantify how often graph-specific checks succeed where universal ones fail.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The manuscript distinguishes two notions of redundant conditional independence (CI) statements in constraint-based graphical model discovery: universal CIs (true for every probability distribution) versus graph-specific CIs (implied only by the Markov properties of a candidate DAG). It claims that only the latter can detect or correct errors arising from unreliable statistical tests, while the former cannot and must be applied with care.

Significance. The clarification of redundancy notions could help practitioners select informative unused CI tests for post-hoc validation of learned graphs. If the distinction is made operational, it offers a conceptual tool for improving robustness of algorithms such as PC or FCI. The paper receives credit for cleanly separating semantic notions of redundancy that had not been explicitly contrasted in prior literature on CI-based discovery.

major comments (1)
  1. [Abstract / main argument] Abstract and main argument: the central claim that universal CIs are 'unlikely to detect and correct errors' is load-bearing yet rests on a purely semantic distinction without a concrete counter-example, finite-sample bound, or simulation demonstrating that tests of universal CIs systematically fail to flag inconsistencies that graph-specific CIs would catch under realistic type-I/II error rates.
minor comments (1)
  1. Notation for the two classes of redundant statements could be introduced earlier and used consistently to improve readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and the opportunity to clarify our central argument. Below we respond to the major comment.

read point-by-point responses

  1. Referee: [Abstract / main argument] Abstract and main argument: the central claim that universal CIs are 'unlikely to detect and correct errors' is load-bearing yet rests on a purely semantic distinction without a concrete counter-example, finite-sample bound, or simulation demonstrating that tests of universal CIs systematically fail to flag inconsistencies that graph-specific CIs would catch under realistic type-I/II error rates.

    Authors: The claim follows directly from the definitions we introduce. A universal CI holds in every probability distribution; therefore an incorrect learned graph cannot cause its violation. Any statistical rejection of a universal CI must be due to test error alone and supplies no evidence that the graph is wrong. By contrast, a graph-specific CI is implied only by the Markov properties of the candidate DAG; its violation is consistent with either test error or an incorrect graph. This is a logical consequence of the two notions rather than an empirical claim requiring bounds or simulations. We nevertheless agree that an explicit illustrative example would make the distinction more accessible and will add one in revision. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: theoretical distinction between universal and graph-specific CIs is self-contained

full rationale

The paper's core argument distinguishes conditional independencies that hold for every probability distribution (universal) from those implied only by the Markov properties of a specific DAG (graph-specific), asserting the former are unlikely to detect or correct errors in learned models. This distinction follows directly from the definitions of the two classes of statements and is presented as a logical consequence without any fitted parameters, predictions that reduce to inputs, or load-bearing self-citations. No equations or derivations in the provided abstract or described content loop back to the inputs by construction; the claim is advanced as a semantic and theoretical observation rather than an empirical or derived result that presupposes itself. The translation to practical error detection is asserted on the basis of this distinction alone and does not rely on prior author work invoked as an external uniqueness theorem or ansatz.

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

Abstract-only review yields no visible free parameters, axioms, or invented entities.

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Cite this review

Pith. "Pith review of On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models." pith.science (2026). https://pith.science/paper/2502.08531

@misc{pith2026250208531,
  author       = {Pith},
  title        = {Pith review of: On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2502.08531}},
  note         = {Machine review of arXiv:2502.08531}
}
read the original abstract

Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to errors and violated assumptions. Often, there are tests that were not used in the construction of the graph. In this work, we show that these redundant tests have the potential to detect or sometimes correct errors in the learned model. But we further show that not all tests contain this additional information and that such redundant tests have to be applied with care. Precisely, we argue that the conditional (in)dependence statements that hold for every probability distribution are unlikely to detect and correct errors - in contrast to those that follow only from graphical assumptions.

Figures

Figures reproduced from arXiv: 2502.08531 by the authors.

Figure 1
Figure 1. The marginal independence tests identify the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Hierarchy of definitions 1 to 3. We argue to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. In this graph, every dependence along more [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: A false test Y ̸⊥⊥ Z may lead to the conclusion that the true graph is X → Y → Z. If this were the only error, the test Y ⊥⊥ Z | X would correct that. But Y ̸⊥⊥ Z | X follows via Graphoid axioms from the marginal tests. such a model exists and thus none of the CI-state…
Figure 5
Figure 5. Figure 5: Experimental results CI-statements that PC would do anyway to detect con￾tradictions without discussing the dependences between tests. (Bromberg and Margaritis, 2009) have also noted that contradictions between CI-tests can be corrected to improve discovery results, an…
Figure 6
Figure 6. Figure 6: Example for a modified graph G′ from the proof of proposition 2. All intermediate nodes on the path X − V − W − Y are replaced by two copies and each new path only connects to either X or Y . To see the former, let (A ⊥⊥ B | C) ∈ L. In our construction we did not add a…
Figure 7
Figure 7. Figure 7: Visualization how the node W from the proof of proposition 3 can be connected to X and Y in case 2 of the proof. Proof (Proof for lemma 2) Concerning (1): Suppose there is not permutation π ∗ such that all CI-statements in Lπ∗ are as in MG∗ . So for every π there is a …
Figure 8
Figure 8. Figure 8: When the collider-structure X0 → X1 ← Z is wrongly identified, all edges Xi+1 → Xi will be oriented the wrong way around. Example 6 (Probabilistically but not Graphoid-redundant) In this example, we will see a case where some CI-statements are probabilistically redunda…
Figure 9
Figure 9. Figure 9: This example illustrates how the PC algorithm arrives at a graph that contradicts a dependence [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 11
Figure 11. Figure 11: A and B are coupled over (X, Y, ∅) given ∅ (as defined in definition 5) but neither A and B nor X and Y are coupled given ∅ (as defined in definition 7) as they are not adjacent. G THE MMD ALGORITHM In the following, we will propose an assumption and an algorithm that…
Figure 12
Figure 12. Figure 12: If this graph is the ground truth but we have the unfaithful independences [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 13
Figure 13. Figure 13: Structural Hamming distance for simplified PC algorithm and MMD algorithm from proposition 3. [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Structural Hamming distance when we modify MMD to optimize over different sets of tests. Additional [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]
Figure 15
Figure 15. Figure 15: Incorrect predictions of purely graphically versus Graphoid redundant CI-statements on synthetic [PITH_FULL_IMAGE:figures/full_fig_p026_15.png]
Figure 16
Figure 16. Figure 16: Ground truth graphs for the experiment in fig. 5b. [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]

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Forward citations

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · cited by 1 Pith paper

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    PMLR, 2024. Jiji Zhang and Peter Spirtes. The three faces of faith- fulness.Synthese, 193:1011–1027, 2016. Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ram- sey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, and Kun Zhang. Causal-learn: Causal discovery in python.Journal of Machine Learning Research, 25(60):1–8, 2024. Checklist

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    For any theoretical claim, check if you include: (a) Statements of the full set of assumptions of all theoretical results. Yes. (b) Complete proofs of all theoretical results. Yes. (c) Clear explanations of any assumptions. Yes

    work page

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    For all figures and tables that present empirical results, check if you include: (a) The code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL). Yes. (b) All the training details (e.g., data splits, hyper- parameters, how they were chosen). Yes. (c) A clear definition of the specifi...

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    If you are using existing assets (e.g., code, data, models) or curating/releasing new assets, check if you include: (a) Citations of the creator If your work uses ex- isting assets. Yes. (b) The license information of the assets, if applic- able. Yes. (c) New assets either in the supplemental material or as a URL, if applicable. Yes. (d) Information about...

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    Not Applicable

    If you used crowdsourcing or conducted research with human subjects, check if you include: (a) The full text of instructions given to parti- cipants and screenshots. Not Applicable. (b) Descriptions of potential participant risks, with links to Institutional Review Board (IRB) ap- provals if applicable. Not Applicable. (c) The estimated hourly wage paid t...

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    Symmetry:X⊥ ⊥Y|Z⇐ ⇒Y⊥ ⊥X|Z

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Show all 18 references
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    Decomposition:X⊥ ⊥Y∪W|Z=⇒X⊥ ⊥Y|Z∧X⊥ ⊥W|Z

  2. [10]

    Weak Union:X⊥ ⊥Y∪W|Z=⇒X⊥ ⊥Y|Z∪W∧X⊥ ⊥W|Z∪Y

  3. [11]

    Contraction:X⊥ ⊥Y|Z∧X⊥ ⊥W|Z∪Y=⇒X⊥ ⊥Y∪W|Z Mis calledgraphoidif we further have

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    Bouckaert (1995) uses the following definition to graphically characterise CI-statements that follow via the Graphoid axioms, as used in corollary 6

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    Analogously addW→W ′ 1 and W→W ′ 2 if W∈V\N (X∼Y| Z)

    If W ′ ∈V\N (X∼Y| Z)add W1 →W ′ and W2 →W ′. Analogously addW→W ′ 1 and W→W ′ 2 if W∈V\N (X∼Y| Z). Finally addX→W 1 and X←W 1 wheneverX→WorX←WforW∈N(X∼Y|Z)andY→W 2 andY←W 2 respectively. Clearly, in this graph we haveX⊥ G′ Y| Z′, whereZ ′ contains all nodes inZ\N (X∼Y| Z)and W...

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    IfYlies betweenXandZ, i.e.X−Y ∗ −Zwe getX̸⊥ T ′ Z|YbutX⊥ T ∗ Z|Y

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    So in any case we have another difference betweenT∗ andT ′

    IfXlies betweenYandZ, i.e.Z ∗ −X−Ywe getZ̸⊥ T ′ Y|XbutZ⊥ T ∗ Y|X. So in any case we have another difference betweenT∗ andT ′. W.l.o.g. we can assume thatZ is the node closest toX on the path betweenX and Y, i.e. the node that has a direct edge toX. Now letW be another node (wh...

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    if we have X−Z ∗ −Y ∗ −W

    IfYis betweenZandW, i.e. if we have X−Z ∗ −Y ∗ −W. Then we haveX⊥ T ′ W|ZbutX̸⊥ T ∗ W|Z, since inT ∗ we had the direct edge fromXtoY

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    if we have the structure in fig

    If Y does not lie on the paths betweenX and W and X not on the one betweenY and W, i.e. if we have the structure in fig. 7, we will further subdivide this into two subcases depending onT∗. (a) InT ∗ we haveYbetweenXandW, i.e.X−Y ∗ −W. ThenX̸⊥ T ′ W|YbutX⊥ T ∗ W|Y. (b) InT ∗ we...

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    W ∗ −X−Z ∗ −Y

    IfXis betweenWandY, i.e. W ∗ −X−Z ∗ −Y. Then this case works symmetrically to the first one. Since we choseW arbitrarily, we can repeat this for every node other thanX, Y, Z. This means, we getn− 3 more contradictions between the independence model ofT ∗ and T ′. So overall we...

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