当没有答案比错误的答案更好时:对批量效应的因果视角
Eric W Bridgeford1,2, Michael Powell1, Gregory Kiar3
1Johns Hopkins University, Baltimore, MD, United States.
Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
概括
本研究引入了一个因果关系框架,以解决多站点数据分析中的批量效应. 因果关系方法可以更准确地识别批量效应,或者在数据不足时适当地说明,从而提高科学可重复性.
科学领域:
- 数据科学数据科学数据科学
- 生物统计学 生物统计学
- 神经成像分析分析 神经成像分析
背景情况:
- 批量效应在多实验数据中引入了变异性,挑战了科学发现和可重复性.
- 传统的统计方法难以区分批量效应和混偏见,导致不准确的结论.
- 科学中的可复制性危机被未解决的批量效应加剧.
研究的目的:
- 将批量效应正式化为因果效应,超越经典的统计建模.
- 开发和验证使用因果推理进行强大的批量效应检测的算法.
- 为了解多站点数据分析的能力和局限性提供一个框架.
主要方法:
- 使用因果推理原则正式化批量效应.
- 开发新的算法,利用因果机制进行批量效应评估.
- 进行模拟并将方法应用于现实世界的神经成像数据集.
主要成果:
- 与非因果方法相比,因果方法提供了更准确的批量效应识别.
- 当不确定时,因果关系方法正确地表明数据不足,而不是强迫得出结论.
- 对神经成像数据的应用揭示了与非因果方法的差异,突出了因果框架的好处.
结论:
- 一个因果框架为识别和管理复杂数据集中的批量效应提供了更可靠的方法.
- 提出的方法提高了多站点数据分析的准确性和可解释性.
- 这项工作阐明了从多个来源分析数据的潜力和局限性.
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