Extrapolating Single-Treatment Effects Out of Factorial Experiments
从析因实验外推单一处理效应AI 解读 ↓
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从析因实验外推单一处理效应
- 研究问题
- 同时随机化多个干预的析因实验，能否识别某一干预单独实施、其他变量自然变化时的效应，何时能够判断该效应的符号？
- 方法与思路
- 作者区分多重干预与单一干预的反事实，给出识别充分条件，讨论线性函数形式及非参数因果图要求，并构造非参数锐界。
- 材料陈述的贡献
- 摘要称析因实验通常不足以直接识别实验外的单一处理效应，并提出识别条件与最具信息性的上下界，用模拟展示符号外推的边界。
- 阅读关注点
- 应核对单一处理反事实中其他变量的自然取值定义、因果图与线性条件的具体作用，以及锐界使用哪些实验信息和额外限制。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未经核实；摘要未给出完整识别条件或界的公式，不能推断所有析因设计均无法外推。
原文摘要
Despite their cost, randomized controlled trials (RCTs) are widely regarded as gold-standard evidence in disciplines ranging from social science to medicine. In recent decades, researchers have increasingly sought to reduce the resource burden of repeated RCTs with factorial designs that simultaneously test multiple hypotheses, e.g. experiments that evaluate the effects of many medications or products simultaneously. Here I show that when multiple interventions are randomized in experiments, the effect any single intervention would have outside the experimental setting is not identified absent heroic assumptions, even if otherwise perfectly realistic conditions are achieved. This happens because single-treatment effects involve a counterfactual world with a single focal intervention, allowing other variables to take their natural values (which may be confounded or modified by the focal intervention). In contrast, observational studies and factorial experiments provide information about potential-outcome distributions with zero and multiple interventions, respectively. In this paper, I formalize sufficient conditions for the identifiability of those isolated quantities. I show that researchers who rely on this type of design have to justify either linearity of functional forms or -- in the nonparametric case -- specify with Directed Acyclic Graphs how variables are related in the real world. Finally, I develop nonparametric sharp bounds -- i.e., maximally informative best-/worst-case estimates consistent with limited RCT data -- that show when extrapolations about effect signs are empirically justified. These new results are illustrated with simulated data.