Sharp Nonparametric Bounds for Decomposition Effects with Two Binary Mediators
两个二元中介下分解效应的尖锐非参数界限AI 解读 ↓
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两个二元中介下分解效应的尖锐非参数界限
- 研究问题
- 随机试验中存在两个顺序二元中介，且中介与结局之间有未测量混杂时，哪些机制效应可以获得尖锐且有效的界限？
- 方法与思路
- 作者针对总风险差的不同中介分解与控制直接效应推导界限，并用因果模型下的随机概率分布模拟及真实数据示例说明。
- 材料陈述的贡献
- 预印本摘要称给出多类中介效应的尖锐界限，并指出直接加减各项尖锐界限通常只能获得有效而不尖锐、甚至无信息的界限。
- 阅读关注点
- 应核对因果模型、效应定义和尖锐性论证，并区分摘要所述主文与补充材料的界限，以及未测量混杂的具体允许范围。
- 阅读边界
- 依据 arXiv 2112.13632v1 预印本摘要，不能据此声称已经同行评审或期刊接收；具体识别条件及刊本变化未说明。
原文摘要
In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathway between the randomized treatment and the outcome. In the setting with two sequential mediators, there are a variety of decompositions of the total risk difference into mediation effects. We derive sharp and valid bounds for a number of mediation effects in the setting of two sequential mediators both with unmeasured confounding with the outcome. We provide five such bounds in the main text corresponding to two different decompositions of the total effect, as well as the controlled direct effect, with an additional thirty novel bounds provided in the supplementary materials corresponding to the terms of twenty-four four-way decompositions. We also show that, although it may seem that one can produce sharp bounds by adding or subtracting the limits of the sharp bounds for terms in a decomposition, this almost always produces valid, but not sharp bounds that can even be completely noninformative. We investigate the properties of the bounds by simulating random probability distributions under our causal model and illustrate how they are interpreted in a real data example.