One Ruler: A Same-Hands Re-Evaluation of Bivariate Causal Direction on Tuebingen, with a Parameter-Free Compression Baseline
arXiv:2606.23767v1 Announce Type: new Abstract: Headline accuracies on the Tuebingen cause-effect pairs are routinely compared across papers even though each is measured under its authors' own protocol -- different pair subsets, weightings, model-selection, and decision rates. We argue this is the wrong comparison and run the right one: a same-hands re-evaluation in which every method is run by us on the identical 102 pairs, with one strict rule -- no tuning and a decision forced on every pair. As a clean reference point we introduce a deliberately minimal baseline: sorted-conditional compress
This paper is a routine publication within the academic cycle of AI research, focusing on improving methodological rigor in evaluating causal direction algorithms.
While contributing to better scientific practice, this specific research does not introduce new capabilities, nor does it significantly alter the broader trajectory of AI development in the near term.
The paper calls for, and demonstrates, a more consistent and robust methodology for comparing causal inference algorithms, which, if adopted, would improve the reliability of academic benchmarks.
- · Academic researchers in causal inference
- · Developers of causal inference algorithms (if they adopt the new methodology)
- · Papers with less rigorous benchmarking methodologies
Improved comparison standards for causal direction algorithms in academic literature are proposed.
Over time, this could lead to more accurate assessments of algorithm performance and accelerate progress in causal AI.
More reliable causal AI could eventually contribute to better decision-making systems in various applications, though this is a very long-term and indirect effect.
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Read at arXiv cs.LG