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.

中文翻译：

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您是否进行了敏感性分析？一种基于加权的新方法，用于评估受治疗者的平均治疗效果

在非实验性研究中，敏感性分析有助于确定因存在隐性偏见而导致的因果结论是否易于逆转。一种基于加权的敏感性分析的新方法扩展并补充了倾向得分加权方法，用于确定被治疗者（ATT）的平均治疗效果。从本质上讲，为忽略的混杂因素调整的新权重与忽略它们的初始权重之间的差异抓住了混杂因素的作用。该策略之所以吸引人，原因有很多，其中包括，无论数据生成函数有多复杂，灵敏度参数的数量仍然很少，并且其形式永远不变。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。对著名的LaLonde数据的应用列出了实现过程，并说明了其广泛的用途。数据提供了对受训人群平均职业培训计划的平均影响的非实验评估的典型示例。