关于"非参数识别是不够的,但随机对照试验是"':在越来越多地涉及真实世界元素的研究频谱中产生可靠证据的统计考虑
Rachael Phillips1, Mark van der Laan1
1Center for Targeted Machine Learning and Causal Inference University of California, Berkeley.
Observational studies
|June 9, 2025
概括
随机对照试验 (RCT) 可能不是因果分析的唯一黄金标准. 在观察性研究中,即使有完美的数据,统计推断也更难,挑战了关于证据可靠性的既定观点.
科学领域:
- 因果推理的原因推理.
- 统计方法学的统计方法.
- 流行病学 流行病学
背景情况:
- 朱迪亚·珍珠的断言,随机对照试验 (RCT) 不是最终的因果分析的黄金标准.
- 阿罗诺夫和其他人. (2024) 驳斥了这一说法,引用了Robins和Ritov (1997) 的说法.
- 辩论的重点是观察性研究与RCT中的统计推断的相对困难.
研究的目的:
- 加入正在进行的关于因果分析的黄金标准辩论.
- 探索在各种研究设计中生成的证据的可靠性.
- 检查现实世界研究的统计方法的挑战.
主要方法:
- 该评论使用Robins和Ritov (1997) 的结果驳斥了Pearl的说法.
- 它分析了与RCT相比观察性研究的统计估计和推断的基本困难.
- 专注于在各种研究环境中可靠的证据生成的要求.
主要成果:
- 在观察性研究中,统计估计和推断本身比在RCT中更具挑战性,即使没有错误的混数据.
- 证据的可靠性并不一定会在越来越多的真实世界元素的各种研究中减少.
- 适当的统计方法的复杂性随着研究设计的控制程度降低而增加.
结论:
- 随机试验仍然至关重要,在观察环境中,统计推断显然更难.
- 可靠的证据可以在各种类型的研究中生成,但需要越来越复杂的统计方法.
- 挑战不在于可靠性下降,而在于对先进而细致的统计执行的需求增加.
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