On the probability of invalidating a causal inference due to limited external validity
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外部效度不足导致因果推断失效的概率
- 研究问题
- 当观测样本不能充分代表目标总体时，如何量化已有显著结论在理想代表性样本中不再显著的概率及其敏感性？
- 方法与思路
- 作者将理想样本与观测样本的差距表述为未观测样本，定义条件于观测样本拒绝原假设的失效概率，并分析相关统计量阈值。
- 材料陈述的贡献
- 摘要提出外部效度敏感性评价程序，以经验案例说明如何寻找使失效概率超过目标值的阈值，并讨论其与统计功效的联系。
- 阅读关注点
- 应核对理想与未观测样本的构造、失效概率的概率来源及计算条件，以及显著性变化如何对应目标总体的效应解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未说明完整概率模型，不再显著也不等同于因果效应消失或方向改变。
原文摘要
External validity is often questionable in empirical research, especially in randomized experiments due to the trade-off between internal validity and external validity. To quantify the robustness of external validity, one must first conceptualize the gap between a sample that is fully representative of the target population (i.e., the ideal sample) and the observed sample. Drawing on Frank & Min (2007) and Frank et al. (2013), I define such gap as the unobserved sample and intend to quantify its relationship with the null hypothesis statistical testing (NHST) in this study. The probability of invalidating a causal inference due to limited external validity, i.e., the PEV, is the probability of failing to reject the null hypothesis based on the ideal sample provided the null hypothesis has been rejected based on the observed sample. This study illustrates the guideline and the procedure of evaluating external validity with the PEV through an empirical example (i.e., Borman et al. (2008)). Specifically, one would be able to locate the threshold of the unobserved sample statistic that would make the PEV higher than a desired value and use this information to characterize the unobserved sample that would render external validity of the research in question less robust. The PEV is shown to be linked to statistical power when the NHST is thought to be based on the ideal sample.