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REVIEW 2 major objections 1 minor 72 references

Learning a directed acyclic graph with additive heteroscedastic errors

T0 review · 2 major / 1 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Heteroscedastic errors identify DAG directions via quantile-invariant scales

desk verdict The paper shows heteroscedasticity aids identifiability in DAG learning via quantile invariance in RESQUE. read the letter →

arxiv 2605.26515 v1 pith:5ZP52B7G submitted 2026-05-26 stat.ME

Xintao Xia , Li Chen , Yue Hu , Chunlin Li This is my paper · ORCID
classification stat.ME
keywords causaldiscoverydirectedacyclicgraphheteroscedasticerrorsquantileregressionstructuralequationmodelidentifiabilitytopologicalorder
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 establishes identifiability results for location-scale noise models on directed acyclic graphs, showing that heteroscedasticity supplies information to recover causal directions. It introduces the RESQUE procedure, an iterative method that constructs residuals and applies composite quantile regression to exploit the invariance of conditional scale coefficients across quantiles and thereby locate sink nodes recursively. A sympathetic reader would care because standard causal discovery often relies solely on mean relationships while ignoring structured variance signals that can resolve edge directions. The procedure carries consistency guarantees that continue to hold when the number of variables grows with the sample size. Simulations indicate stronger performance precisely when causal information resides in the variance component.

What carries the argument

The invariance of conditional scale coefficients across quantiles in the location-scale noise model, used by the RESQUE iterative procedure to recursively identify sink nodes.

What would settle it

Generate data from a known DAG under additive heteroscedastic errors but with scale coefficients that deliberately vary across quantiles; the procedure should then fail to recover the correct topological order.

Watch

Extended reading notes

Core claim

Under a structural equation model with additive heteroscedastic errors, the conditional scale coefficients remain invariant across quantiles. This invariance permits the RESQUE procedure to identify sink nodes iteratively via residual construction and composite quantile regression, recovering both the topological order and the full graph structure, with theoretical consistency even when the number of variables diverges with the sample size.

Load-bearing premise

Conditional scale coefficients remain unchanged regardless of the quantile level examined.

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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

2 major / 1 minor

Summary. The paper studies causal discovery for DAGs under a structural equation model with additive heteroscedastic errors. It establishes new identifiability results showing that heteroscedasticity can be leveraged to recover causal directions via quantile-invariant conditional scale coefficients. It proposes the RESQUE iterative procedure (residual construction followed by composite quantile regression) for recursive sink-node identification, proves theoretical guarantees for topological order and graph recovery even when p diverges with n, and reports favorable simulation and benchmark performance relative to existing methods when variance components carry causal information.

Significance. If the identifiability and high-dimensional consistency results hold, the work provides a principled extension of causal discovery beyond mean-based modeling by exploiting structured variance signals. This is potentially valuable in domains where heteroscedasticity encodes directional information, and the allowance for diverging p broadens applicability.

major comments (2)
  1. [Abstract] Abstract (identifiability paragraph): the central claim that 'heteroscedasticity can be leveraged to recover causal directions' rests on the location-scale model with quantile-invariant conditional scales; without the explicit theorem statement, assumptions, and proof, it is impossible to verify whether the invariance holds generically or only under additional restrictions that may not be stated.
  2. [Abstract] Abstract (theoretical guarantees paragraph): the claim of 'theoretical guarantees for recovering topological order and graph structure, even when the number of variables diverges with the sample size' is load-bearing for the paper's contribution; the provided text gives no derivation, rate conditions, or proof sketch, leaving the soundness of the high-dimensional result unverified.
minor comments (1)
  1. The abstract mentions simulation studies and benchmark applications but provides no details on data exclusion rules, simulation design, or performance metrics; these should be expanded for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments. We address the two major comments on the abstract below by directing to the corresponding formal results in the manuscript. The abstract is intended as a concise summary; the full statements, assumptions, and proofs appear in the body of the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract (identifiability paragraph): the central claim that 'heteroscedasticity can be leveraged to recover causal directions' rests on the location-scale model with quantile-invariant conditional scales; without the explicit theorem statement, assumptions, and proof, it is impossible to verify whether the invariance holds generically or only under additional restrictions that may not be stated.

    Authors: The identifiability result is stated precisely as Theorem 3.1 in Section 3. Under the location-scale structural equation model and assumptions (A1)–(A3), the theorem establishes that the conditional scale coefficients are invariant across quantiles if and only if the corresponding edge is absent. The proof in Appendix A shows that the invariance property holds under these model assumptions without further restrictions. The abstract condenses this result; the explicit statement, assumptions, and proof are provided in the main text. revision: no

  2. Referee: [Abstract] Abstract (theoretical guarantees paragraph): the claim of 'theoretical guarantees for recovering topological order and graph structure, even when the number of variables diverges with the sample size' is load-bearing for the paper's contribution; the provided text gives no derivation, rate conditions, or proof sketch, leaving the soundness of the high-dimensional result unverified.

    Authors: The high-dimensional consistency results appear as Theorem 4.2 and Corollary 4.3 in Section 4. These establish recovery of the topological order and graph structure when p diverges with n, subject to explicit rate conditions (p = o(n^{1/3}) under sub-Gaussian tails). A proof sketch is given in the main text of Section 4, with the complete derivation in Appendix B. The abstract summarizes these guarantees; the rate conditions and proofs are contained in the manuscript. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The abstract and context describe identifiability results derived from the location-scale structural equation model assumptions (additive heteroscedastic errors with quantile-invariant conditional scales) and the RESQUE iterative procedure for sink-node identification. No equations, proofs, or self-citations are supplied that reduce any claimed prediction or first-principles result to fitted inputs by construction. The theoretical guarantees for topological order recovery (even with diverging p) are presented as following from the model properties rather than from renaming or self-referential fitting. This is the expected self-contained case.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Ledger constructed from abstract only; full paper details on parameters and assumptions unavailable.

assumptions (1)
  • domain assumption Data generated from structural equation model with additive heteroscedastic errors
    Explicitly stated as the model class for which new identifiability results are derived.

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Cite this review

Pith. "Pith review of Learning a directed acyclic graph with additive heteroscedastic errors." pith.science (2026). https://pith.science/paper/5ZP52B7G

@misc{pith2026260526515,
  author       = {Pith},
  title        = {Pith review of: Learning a directed acyclic graph with additive heteroscedastic errors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ZP52B7G}},
  note         = {Machine review of arXiv:2605.26515}
}
read the original abstract

This paper studies causal discovery for a directed acyclic graph under a structural equation model with additive heteroscedastic errors. We first establish new identifiability results for location-scale noise models, showing that heteroscedasticity can be leveraged to recover causal directions. Based on these insights, we propose a novel iterative procedure, Residual Simultaneous Quantile Estimation (RESQUE), where each iteration consists of a residual-construction stage and a composite quantile regression stage, enabling recursive identification of sink nodes via the invariance of conditional scale coefficients across quantiles. We then establish its theoretical guarantees for recovering topological order and graph structure, even when the number of variables diverges with the sample size. Simulation studies and application to benchmark datasets show that RESQUE performs favorably compared with existing methods, especially when causal information is partly encoded in the variance component. These results highlight exploiting structured variance signals for causal discovery and provide a principled framework for multivariate causal discovery beyond mean-based modeling.

Figures

Figures reproduced from arXiv: 2605.26515 by the authors.

Figure 1
Figure 1. Illustration of the topological-layer decomposition of a DAG. [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
Figure 2
Figure 2. Empirical performance of CAM, rank-PC, TL, NOTEARS, and the proposed [PITH_FULL_IMAGE:figures/full_fig_p023_2.png] view at source ↗
Figure 3
Figure 3. Empirical performance of CAM, rank-PC, TL, NOTEARS, and RESQUE in [PITH_FULL_IMAGE:figures/full_fig_p024_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Consensus graph and estimated graph by RESQUE using the Sachs dataset. [PITH_FULL_IMAGE:figures/full_fig_p026_4.png]

Discussion (0). Continue with ORCID to comment.

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