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Writing study: On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding

← ICML 2026 oral writing corpus

Jie Qiao, Zihuai Zeng, Ruichu Cai, Zhengming Chen, Zhifeng Hao · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Theory & formal analysis (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
30 physical pages
Before references / appendix
≈ 9.32 page-equivalents
Main figures / tables
6 / 1
Attached appendix figures / tables
4 / 6
Attached appendix span
≈ 18.88 page-equivalents
Before references / appendix: ≈ 31.07%References / intervening material: ≈ 6%Attached appendix: ≈ 62.93%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Join wrapped printed heading text using adjacent source lines; original heading start coordinates are unchanged.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 1.37 pp.4.57%
2. Latent Confounding Poisson Branching Structural Causal Model2≈ 1.6 pp.5.33%
3. Identifiability4≈ 4.32 pp.14.4%
4. Practical algorithm8≈ 0.2 pp.0.67%
5. Experiments8≈ 0.91 pp.3.03%
6. Conclusion9≈ 0.27 pp.0.9%
Acknowledgments10≈ 0.12 pp.0.4%
Impact Statement10≈ 0.16 pp.0.53%
References10≈ 1.79 pp.5.97%
Appendix12≈ 0.6 pp.2%
A. Closed-Form Solution for PGF of LC-PB-SCM12≈ 1.38 pp.4.6%
B. Proof of Identifiability14≈ 11.42 pp.38.07%
C. Additional Experiments25≈ 2.17 pp.7.23%
D. Additional Discussion of Complexity27≈ 0.14 pp.0.47%
E. Additional Discussion of Robustness27≈ 1.27 pp.4.23%
F. Additional Discussion of Binomial Thinning in the Presence of Latent Variables29≈ 1.91 pp.6.37%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Model and Preliminaries

    Defines branching counts, exogenous latent roots and the partial-graph recovery target.

  2. 3 Identifiability

    Uses a motivating asymmetry, Trie representation and local monomial rules to state completeness.

  3. 4 Algorithm; 5 Experiments; 6 Conclusion

    Translates algebraic signatures into empirical derivative tests and evaluates finite-sample examples.

Problem, claim and contribution

The paper explicitly excludes intermediate latent mediators and targets an equivalence-class graph rather than the full latent DAG.

Problem formulation and Goal, p.3 · Main paper

Argument and section order

A worked pair of graphs reveals the distinguishing monomial before the general representation theorem and rule family are introduced.

Sections 3.1-3.3, pp.4-6 · Main paper

Figures, captions and visual explanation

The construction diagram links colored graph vertices, pruned paths and algebraic terms; the rule atlas highlights target edges in blue and prints the theorem and monomial condition above each case.

Figure 2, p.5; Figure 3, p.7 · Main paper

Tables, comparisons and evidence

The completeness claim is explicitly for local three-node structures under monomial equivalence, while the algorithm must estimate noisy higher-order derivatives.

Theorem 3.13 and Section 4, p.8 · Main paper

Appendix and reproducibility

The larger-graph results retain low-sample losses to FCI, and the football illustration discloses use of a supplied skeleton rather than end-to-end graph discovery.

Appendix C.2 and Tables 3-4, p.27; robustness example, p.28 · Main paper

Clarity and writing conventions

The main text distinguishes local zero-setting from latent marginalization at one, helping prevent two mathematically different operations from being conflated.

Definitions 2.4-2.5 and Property 2.6, p.4 · Main paper

What to reuse—and what not to infer

Transferable lesson: Turn a family of identification rules into a visual atlas while keeping local completeness, global reconstruction and noisy estimation as separate claims.

Boundary: The guarantee depends on Poisson branching, independent latent roots and stated graph assumptions; local monomial completeness is not unrestricted global identifiability.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-07
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13, 27, 28. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
5, 7

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.