Moderating the Mediation Bootstrap for Causal Inference
因果中介分析中Bootstrap推断的校准问题AI 解读 ↓
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因果中介分析中Bootstrap推断的校准问题
- 研究问题
- 简单中介模型中的间接效应区间为何出现过度保守或覆盖不足，配对、残差、偏差校正及双重Bootstrap的表现又有何差异？
- 方法与思路
- 作者开展大规模模拟考察多种Bootstrap方案，并结合图形分析与新推导的有限样本分布，解释区间覆盖率对估计系数的敏感性。
- 材料陈述的贡献
- 摘要称Bootstrap分布形状高度依赖估计系数，导致保守表现及双重Bootstrap的反向校正；偏差校正法在小中介效应下仍可能覆盖不足。
- 阅读关注点
- 应核对简单中介模型的分布与因果假设、有限样本分布的适用范围，以及零效应附近不同区间构造的覆盖率和计算成本。
- 阅读边界
- 这是arXiv预印本，不能据此认定已同行评审或期刊接收。摘要未说明完整模拟设置与分布条件，结论不能直接推广至复杂中介模型。
原文摘要
Mediation analysis is a form of causal inference that investigates indirect effects and causal mechanisms. Confidence intervals for indirect effects play a central role in conducting inference. The problem is non-standard leading to coverage rates that deviate considerably from their nominal level. The default inference method in the mediation model is the paired bootstrap, which resamples directly from the observed data. However, a residual bootstrap that explicitly exploits the assumed causal structure (X->M->Y) could also be applied. There is also a debate whether the bias-corrected (BC) bootstrap method is superior to the percentile method, with the former showing liberal behavior (actual coverage too low) in certain circumstances. Moreover, bootstrap methods tend to be very conservative (coverage higher than required) when mediation effects are small. Finally, iterated bootstrap methods like the double bootstrap have not been considered due to their high computational demands. We investigate the issues mentioned in the simple mediation model by a large-scale simulation. Results are explained using graphical methods and the newly derived finite-sample distribution. The main findings are: (i) conservative behavior of the bootstrap is caused by extreme dependence of the bootstrap distribution's shape on the estimated coefficients (ii) this dependence leads to counterproductive correction of the the double bootstrap. The added randomness of the BC method inflates the coverage in the absence of mediation, but still leads to (invalid) liberal inference when the mediation effect is small.