In observational studies, the identification of causal estimands depends on the no unmeasured confounding (NUC) assumption. As this assumption is not testable from observed data, sensitivity analysis plays an important role in observational studies to investigate the impact of unmeasured confounding on the causal conclusions. In this paper, we proposed a risk-ratio-based sensitivity analysis framework by introducing a modified marginal sensitivity model for observational studies with binary treatments. We further extended the proposed framework to the multivalued treatment setting.We then showed how the point estimate intervals and the corresponding percentile bootstrap confidence intervals can be constructed efficiently under the proposed framework. Simulation results suggested that the proposed framework of sensitivity analysis performs well in the presence of adequate overlap among the treatment groups. Lastly, we demonstrated our proposed sensitivity analysis framework by estimating the causal effect of maternal education on female fertility in Bangladesh.

翻译：在观察性研究中，因果估计量的识别依赖于无未测量混杂（NUC）假设。由于该假设无法通过观测数据进行检验，敏感性分析在观察性研究中扮演着重要角色，用于探究未测量混杂对因果结论的影响。本文针对二元处理变量的观察性研究，通过引入修正的边际敏感性模型，提出了一个基于风险比的敏感性分析框架。我们进一步将该框架扩展至多值处理设定。随后，我们展示了如何在该框架下高效构建点估计区间及相应的百分位自助法置信区间。模拟结果表明，当处理组间存在充分重叠时，本文提出的敏感性分析框架表现良好。最后，我们通过评估孟加拉国母亲教育对女性生育率的因果效应，展示了所提敏感性分析框架的应用。