用统一标准重评因果推断方法，发现零参数压缩基线表现最佳。
One Ruler: A Same-Hands Re-Evaluation of Bivariate Causal Direction on Tuebingen, with a Parameter-Free Compression Baseline
- 所有方法在相同102对数据上统一运行，禁止调参并强制决策
- 零参数压缩基线达74.7%准确率，超越多数论文报告结果
- 揭露发表结果被高估的根源：测试集选模与选择性弃权
Tuebingen因果对的性能常被不同论文间比较，但各研究采用各自协议（子集、权重、模型选择、决策率）导致不可比。本文提出同一手重评估：我们统一运行所有方法于相同的102对数据，严格禁用调参且强制每对决策。引入一个零参数基线——排序-条件压缩：将量化、排序、一阶差分后的数据输入现成压缩器（bz2）。在统一标准下，排名显著不同于文献。该基线达74.7%加权准确率（p=3.7e-7）；在与SLOPE同组的100对上得76.0%，仅比作者报告的强制决策版本低1.2点（77.2%），差异不显著（McNemar p=0.39）。RECI的复现结果为70.7%，落在原作者误差范围内，而非常被引用的77.5%（此误源于单元格复制错误）。SLOPE的82.4%是子集选择结果：仅在显著性检验选出的对上评分可复现为81.7%。在统一标准下，各方法集中于70%~75%区间，零参数压缩基线与最强方法持平。本文揭示了发布结果被高估的机制（测试集选模、显著性筛选弃权），并贡献两点新发现：压缩分数大小是模型无关的混淆标志（p=2.8e-68），预注册假验证失败，反向限定了方法理论解释范围。代码、预注册及每对输出均已公开。
原文摘要 · Abstract (English)
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 compression, which feeds quantized, sorted, first-differenced data to an off-the-shelf compressor (bz2) and has zero fitted parameters. Under the common ruler the ranking differs sharply from the literature. Our baseline reaches 74.7% weighted accuracy (p = 3.7e-7); on the same 100 pairs that SLOPE is evaluated on it scores 76.0%, a 1.2-point gap below the authors' own forced-decision SLOPE (77.2%) that is well inside noise (McNemar p = 0.39). A faithful re-run of RECI lands at 70.7% -- inside the original authors' reported error bar, not the 77.5% often quoted (which we trace to a mis-copied cell). SLOPE's published 82.4% is a decided-subset figure: scoring the authors' own stored output only on the pairs its significance test chose to answer reproduces 81.7%. Under the common ruler the methods cluster in the low-to-mid 70s and the zero-parameter compressor ties the strongest of them. We document the mechanisms that inflate published figures (test-set model selection, significance-gated abstention) and contribute two further results: compression score magnitude is a model-free confounding flag (p = 2.8e-68), and a pre-registered falsification test fails in an instructive way that bounds the method's theoretical interpretation. Code, pre-registrations, and per-pair outputs are released.
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