Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to obtain weights that directly minimizes covariate imbalance. In particular, we adapt a sensitivity analysis framework using the percentile bootstrap for a broad class of balancing weights estimators. We prove that the percentile bootstrap procedure can, with only minor modifications, yield valid confidence intervals for causal effects under restrictions on the level of unmeasured confounding. We also propose an amplification to allow for interpretable sensitivity parameters in the balancing weights framework. We illustrate our method through extensive real data examples.

翻译：对未测量混杂因素的敏感性评估是观察性研究中的重要步骤，这类研究通常在假设所有混杂因素均已测量的条件下估算因果效应。本文针对平衡权重估计量（一种通过求解优化问题直接最小化协变量不平衡性的日益流行的估计方法）开发了敏感性分析框架。具体而言，我们基于百分位自助法将敏感性分析框架推广至一大类平衡权重估计量，并证明该方法仅需微小调整即可在限制未测量混杂水平条件下获得因果效应的有效置信区间。同时，我们提出一种放大技术，使平衡权重框架中的敏感性参数具有可解释性。通过大量真实数据实例验证了所提方法的有效性。