Skip to content

Writing study: DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

← ICML 2026 oral writing corpus

Emre Kavak, Tom Nuno Wolf, Christian Wachinger · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Training, objectives & optimization (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
≈ 9.23 page-equivalents
Main figures / tables
4 / 3
Attached appendix figures / tables
8 / 4
Attached appendix span
≈ 17.91 page-equivalents
Before references / appendix: ≈ 30.77%References / intervening material: ≈ 9.53%Attached appendix: ≈ 59.7%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Replace a prose cross-reference falsely detected as a caption with the actual caption from physical-page source text and coordinates.

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≈ 1.6 pp.5.33%
2. Causal Invariance2≈ 2.24 pp.7.47%
3. Distance Correlation for Conditional Independence5≈ 2 pp.6.67%
4. Experiments7≈ 2.13 pp.7.1%
5. Conclusion9≈ 0.41 pp.1.37%
6. Limitations and Ethical Considerations9≈ 0.38 pp.1.27%
Acknowledgment10≈ 0.07 pp.0.23%
Impact Statement10≈ 0.12 pp.0.4%
References10≈ 2.85 pp.9.5%
Appendix13≈ 0.03 pp.0.1%
A. Use of LLMs13≈ 0.11 pp.0.37%
B. Camera-Ready Changes13≈ 0.43 pp.1.43%
C. Contextualization and Related Work13≈ 1.75 pp.5.83%
D. Causal Graph and Assumptions15≈ 0.16 pp.0.53%
E. Causal Invariance as Conditional Independence15≈ 3.86 pp.12.87%
F. Proofs and Supplementary Definitions for Conditional Distance Correlation Background19≈ 2.67 pp.8.9%
G. Exact Factorization and Proof of sDISCO22≈ 2 pp.6.67%
H. Experiments24≈ 6.91 pp.23.03%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Causal Invariance

    Defines the causal graph, desired stability and assumptions before the optimization proxy.

  2. 3 Conditional Distance Correlation

    Moves from population definitions to finite-sample estimators and a practical regularizer.

  3. 4 Experiments; 5 Conclusion; 6 Limitations

    Separates balanced-test accuracy from controlled counterfactual analysis and observed-bias restrictions.

Problem, claim and contribution

The model treats selected mediators as unwanted shortcuts but explicitly allows stable useful mediators to be excluded from the bias set.

Introduction, p.1; Selective Mediation, p.4 · Main paper

Argument and section order

The causal theorem is followed immediately by positivity, selective-mediation and unobserved-confounding discussion before estimator construction.

Sections 2.1-3.1, pp.4-5 · Main paper

Figures, captions and visual explanation

Counterfactual sensitivity and target accuracy are paired beside the causal graph, while the main table shades naive backbones and labels distinct regression, balanced-accuracy and worst-group metrics.

Table 3 and Figure 4, p.8; Table 2, p.9 · Main paper

Tables, comparisons and evidence

Bias labels are used during training but not inference, and true counterfactual evaluation is confined to a controlled generator rather than inferred for every real dataset.

Section 3.3, p.6; Section 4.4, p.8 · Main paper

Appendix and reproducibility

A camera-ready disclosure records an earlier estimator implementation error, preserves old results and identifies the corrected main-text version.

Appendix B, p.13; complementary counterfactual analysis, p.29 · Main paper

Clarity and writing conventions

The limitation section separates theoretical assumptions from practical observed-label needs; stability is explicitly not sufficient for predictive usefulness.

Theorem discussion, p.4; Section 6, p.9 · Main paper

What to reuse—and what not to infer

Transferable lesson: Put assumption failures before the practical estimator and preserve version-specific corrections so readers can distinguish the theorem, surrogate and implementation.

Boundary: A finite regularization penalty is not automatic satisfaction of the population constraint; unknown biases remain, and benchmark demographic labels are imperfect proxies rather than personal identity.

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, 29. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
8, 9

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.