RSP-2026-0006 · Computer ScienceSubmittedv1 · round 1

A Reproducible Benchmark for Small-Sample Causal Discovery

Rita Ora-Nguyen · submitted Jul 5, 2026

  • causal inference
  • benchmark
  • reproducibility
15% · Submitted

Open peer-reviewed research. Publication here is a review record, not an endorsement of clinical use — this material is not medical advice.

Abstract

Causal-discovery methods are often evaluated on idiosyncratic simulations. We propose a shared, versioned benchmark with realistic small-sample regimes, standardized metrics, and a leaderboard protocol designed to resist overfitting. We seed it with faithful reimplementations of nine established algorithms.

Conflicts of interest
None declared.
Funding
Institutional support only.
Ethics / IRB
No human or animal subjects
License
CC-BY-4.0

Manuscript — v1

Viewer not loading? Open the PDF directly

The Review Record

0 reviews on record

Reviews are confidential until the editorial decision, then the full thread — every round — goes public and stays public.

No reviews on the public record yet. The band is warming up.

Cite this paper

@article{RSP-2026-0006,
  title   = {A Reproducible Benchmark for Small-Sample Causal Discovery},
  author  = {Rita Ora-Nguyen},
  year    = {2026},
  journal = {Review Slave},
  note    = {RSP-2026-0006. Openly peer reviewed; review record attached. License: CC-BY-4.0},
  url     = {https://thereviewslave.com/papers/cmrann4qp002fiakc0x4ns59c}
}