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Writing study: Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects

← NeurIPS 2026 oral writing corpus

Chongguang Tao, Zheng Zhou, Yuhong Yang · Official NeurIPS 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
≈ 9.09 page-equivalents
Main figures / tables
5 / 0
Attached appendix figures / tables
1 / 16
Attached appendix span
≈ 18.91 page-equivalents
Before references / appendix: ≈ 30.3%References / intervening material: ≈ 6.67%Attached appendix: ≈ 63.03%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Top-level heading and reference/appendix boundaries checked against source PDF text. Bibliography titles/author initials, numbered contribution/property bullets, diagram steps, equations and TOC entries are not substantive headings. Section spans are page-coordinate estimates, not ink area or prose-only length. This is an author manuscript mirror, not a verified accepted-version template. Source layout uses physical single-column y/page-height, including equation-heavy pages.

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≈ 0.97 pp.3.23%
2 Main Results2≈ 2.18 pp.7.27%
3 Reviewing Some Previous Methods4≈ 2.82 pp.9.4%
4 Experiments7≈ 2.01 pp.6.7%
5 Conclusions9≈ 0.58 pp.1.93%
References10≈ 2 pp.6.67%
A Proof of Main Theorems12≈ 6.67 pp.22.23%
B Definitions of Stochastic Orders18≈ 0.68 pp.2.27%
C Detailed Experimental Setup and Numerical Results19≈ 11.56 pp.38.53%
All detected figure and table locations

Argument and organization

  1. Problem and contribution

    Distinguishes uncertainty for individual treatment effects from easier counterfactual or conditional-mean targets.

  2. Argument sequence

    States impossibility under standard assumptions, then a positive result under additional structure before revisiting prior guarantees.

  3. Evidence and verification

    Simulations illustrate the theorem's implications across dependence and propensity regimes rather than serving as the proof of impossibility.

Problem, claim and contribution

Distinguishes uncertainty for individual treatment effects from easier counterfactual or conditional-mean targets.

Introduction; Section 2.1 · Main paper

Argument and section order

States impossibility under standard assumptions, then a positive result under additional structure before revisiting prior guarantees.

Sections 2–3 · Main paper

Figures, captions and visual explanation

Coverage-versus-target panels include the identity line, and interval-length panels share the scenario layout; separate boxplots show correlated-error stress cases.

Figures 2–3, p. 8 · Main paper

Tables, comparisons and evidence

Simulations illustrate the theorem's implications across dependence and propensity regimes rather than serving as the proof of impossibility.

Section 4 · Main paper

Appendix and reproducibility

Technical lemmas, proof details, stochastic-order definitions and full coverage/length tables keep distinct verification tasks separate.

Appendices A–C · Main paper

Clarity and writing conventions

The positive construction is explicitly described as restrictive, preventing a local exception from blurring the general impossibility statement.

Section 2.3, p. 3 · Main paper

What to reuse—and what not to infer

Transferable lesson: Pair an impossibility statement with the exact assumptions that restore a useful result, then visualize both validity and informativeness.

Boundary: Bad performance in selected simulations alone cannot establish impossibility; the theorem is about a specified distribution class and coverage target.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-02
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-oriented distributed manuscript scan of Sections 1–5, pp. 1–9; references pp. 10–11: abstract/framing, section sequence, substantive text windows, captions, evidence organization and conclusion; not exhaustive technical verification.
Appendix / supplement
Writing-oriented scan of Appendices A–C, pp. 12–30; proofs, stochastic-order definitions, experimental specifications and full tables; code and experiments were not executed.
Visually inspected PDF pages
3, 8

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.