Did you Conduct a Sensitivity Analysis? A New Weighting-Based Approach for Evaluations of the Average Treatment Effect for the Treated
Did you Conduct a Sensitivity Analysis? A New Weighting-Based Approach for Evaluations of the Average Treatment Effect for the Treated
复制标题
您进行过敏感性分析吗？
DOI：
10.1111/rssa.12621
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Qin, Xu
中科院分区：
文献类型：
--
作者：
Hong, Guanglei;Yang, Fan;Qin, Xu
在非实验研究中，敏感性分析有助于确定在存在隐藏偏见的情况下，因果结论是否容易逆转。一种新的方法，敏感性分析的基础上加权扩展和补充倾向评分加权方法，以确定平均治疗效果的治疗（ATT）。从本质上讲，调整遗漏的混杂因素的新权重与忽略它们的初始权重之间的差异捕获了混杂因素的作用。该策略具有吸引力的原因有很多，包括无论数据生成函数有多复杂，敏感性参数的数量仍然很小，并且它们的形式永远不会改变。灵敏度参数值的图形显示有助于对主导电位偏差进行整体评估。著名的LaLonde数据的应用程序奠定了实施程序，并说明了其广泛的实用性。这些数据提供了一个典型的例子，说明对职业培训方案对参与者的平均影响进行的非实验性评价。
In non-experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that adjusts for the omitted confounders and an initial weight that omits them captures the role of the confounders. This strategy is appealing for a number of reasons including that, regardless of how complex the data generation functions are, the number of sensitivity parameters remains small and their forms never change. A graphical display of the sensitivity parameter values facilitates a holistic assessment of the dominant potential bias. An application to the well-known LaLonde data lays out the implementation procedure and illustrates its broad utility. The data offer a prototypical example of non-experimental evaluations of the average impact of job training programmes for the participant population.