用预测提升统计推断，让检验结果更灵活可靠
Prediction-Powered E-Values
- 将预测与e值结合，实现任意时间有效的推断
- 可扩展至变点检测、因果发现等复杂任务
- 模块化设计，适合集成到现有算法中
高质量统计推断依赖充足数据，但数据常缺失或难获取。预测驱动推断作为一种新方法，此前主要局限于均值和分位数等Z估计问题。本文将该思想拓展至e值，继承e值的任意时间有效性、事后有效性及灵活的序贯推断优势，显著扩大了可实现的推断范围。我们证明，只要能用e值表述的推断过程，均可通过本方法获得预测驱动版本。在多种任务中验证了其有效性，包括简单假设检验、置信区间，以及此前无法实现的变点检测和因果发现。该方法模块化强，易于嵌入现有算法，具备实际应用价值。
原文摘要 · Abstract (English)
Quality statistical inference requires a sufficient amount of data, which can be missing or hard to obtain. To this end, prediction-powered inference has risen as a promising methodology, but existing approaches are largely limited to Z-estimation problems such as inference of means and quantiles. In this paper, we apply ideas of prediction-powered inference to e-values. By doing so, we inherit all the usual benefits of e-values -- such as anytime-validity, post-hoc validity and versatile sequential inference -- as well as greatly expand the set of inferences achievable in a prediction-powered manner. In particular, we show that every inference procedure that can be framed in terms of e-values has a prediction-powered counterpart, given by our method. We showcase the effectiveness of our framework across a wide range of inference tasks, from simple hypothesis testing and confidence intervals to more involved procedures for change-point detection and causal discovery, which were out of reach of previous techniques. Our approach is modular and easily integrable into existing algorithms, making it a compelling choice for practical applications.
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