Vaccine randomized trials are typically designed to be blinded, ensuring that the estimated vaccine efficacy (VE) reflects the immunological effect of the vaccine. When blinding is broken, however, the estimated VE reflects not only the immunological effect but also behavioral effects stemming from participants' awareness of their treatment status. Recent work has proposed alternative causal estimands to the standard VE to address this issue, but their point identification results require a strong assumption: the absence of unmeasured common causes of infection risk and participants' belief about whether they received the vaccine. Personality traits, for example, may plausibly violate this assumption. We relax this assumption and derive nonparametric causal bounds for different types of VE. We construct these bounds using two approaches: linear programming-based and monotonicity-based methods. We further consider several possible causal structures for vaccine trials and show how the nonparametric bounds differ across these scenarios. Finally, we illustrate the performance of the proposed bounds using fully synthetic data and a semi-synthetic data example based on a COVID-19 vaccine trial.

翻译：疫苗随机试验通常设计为双盲，以确保估计的疫苗效力（VE）反映疫苗的免疫学效应。然而，当双盲被打破时，估计的VE不仅反映免疫学效应，还包含参与者知晓其治疗状态所引发的行为效应。近期研究提出了替代标准VE的因果估计量以解决此问题，但其点识别结果依赖于一个强假设：感染风险与参与者对是否接种疫苗的信念之间不存在未观测的共因。例如，人格特质可能违反该假设。我们放宽此假设，推导了不同类型VE的非参数因果界。我们采用两种方法构建这些界：基于线性规划的方法和基于单调性的方法。我们进一步考虑了疫苗试验的几种可能因果结构，并展示了非参数界在这些情境下的差异。最后，我们通过完全合成数据和基于COVID-19疫苗试验的半合成数据示例，说明了所提界的性能。