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
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 heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 1.6 pp. | 5.33% |
| 2. Causal Invariance | 2 | ≈ 2.24 pp. | 7.47% |
| 3. Distance Correlation for Conditional Independence | 5 | ≈ 2 pp. | 6.67% |
| 4. Experiments | 7 | ≈ 2.13 pp. | 7.1% |
| 5. Conclusion | 9 | ≈ 0.41 pp. | 1.37% |
| 6. Limitations and Ethical Considerations | 9 | ≈ 0.38 pp. | 1.27% |
| Acknowledgment | 10 | ≈ 0.07 pp. | 0.23% |
| Impact Statement | 10 | ≈ 0.12 pp. | 0.4% |
| References | 10 | ≈ 2.85 pp. | 9.5% |
| Appendix | 13 | ≈ 0.03 pp. | 0.1% |
| A. Use of LLMs | 13 | ≈ 0.11 pp. | 0.37% |
| B. Camera-Ready Changes | 13 | ≈ 0.43 pp. | 1.43% |
| C. Contextualization and Related Work | 13 | ≈ 1.75 pp. | 5.83% |
| D. Causal Graph and Assumptions | 15 | ≈ 0.16 pp. | 0.53% |
| E. Causal Invariance as Conditional Independence | 15 | ≈ 3.86 pp. | 12.87% |
| F. Proofs and Supplementary Definitions for Conditional Distance Correlation Background | 19 | ≈ 2.67 pp. | 8.9% |
| G. Exact Factorization and Proof of sDISCO | 22 | ≈ 2 pp. | 6.67% |
| H. Experiments | 24 | ≈ 6.91 pp. | 23.03% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Causal Invariance
Defines the causal graph, desired stability and assumptions before the optimization proxy.
- 3 Conditional Distance Correlation
Moves from population definitions to finite-sample estimators and a practical regularizer.
- 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.