Sensitivity analysis asks how strong unmeasured confounding needs to be to explain away an observational study's conclusion. The conventional approach in matched studies conducts inference conditional upon the potential outcomes as well as both observed and unobserved confounders, and then finds the worst-case distribution for the conditional treatment assignments across all possible realizations of the unobserved confounder. The resulting worst-case allocation imagines strong, near perfect, correlations between the potential outcomes and hidden bias. We propose a stochastic sensitivity analysis that instead targets inference conditional upon potential outcomes and observed confounders while treating the hidden confounders as random with unknown conditional laws. Rather than finding the worst-case realizations for the hidden confounders, we instead determine the worst-case conditional law over a broad class of distributions. This preserves the adversarial spirit of sensitivity analysis while allowing for imperfect alignment between hidden bias and potential outcomes to a degree controlled by a scalar sensitivity parameter. We consider restrictions to both an interpretable class with no parametric assumptions and a Bernoulli class of conditional laws. Design sensitivity calculations and real-data demonstrations illustrate that allowing for even a small degree of stochasticity can materially increase reported robustness to hidden bias relative to the conventional approach.

翻译：敏感性分析旨在评估未测量混杂因素需达到何种强度，才能推翻观察性研究的结论。传统方法在匹配研究中基于潜在结果及观测与未观测混杂因素进行条件推断，进而寻找所有未观测混杂因素可能实现下条件处理分配的最坏情况分布。这种最坏情况分配假定了潜在结果与隐藏偏差之间存在强烈、近乎完美的相关性。我们提出一种随机敏感性分析方法，该方法转而基于潜在结果与观测混杂因素进行条件推断，同时将隐藏混杂因素视为具有未知条件分布的随机变量。不同于寻找隐藏混杂因素的最坏情况实现，我们在广泛分布类别中确定最坏情况条件分布。此举既保持了敏感性分析的对抗性本质，又允许隐藏偏差与潜在结果之间存在由标量敏感性参数控制程度的非完美对齐。我们分别考虑了无参数假设的可解释条件分布类与伯努利条件分布类。设计敏感性计算与真实数据演示表明：相较于传统方法，即便允许极小程度的随机性，也能显著提升报告中对隐藏偏差的稳健性。