Sensitivity analysis for unmeasured confounding under incremental propensity score interventions remains relatively underdeveloped. Incremental interventions define stochastic treatment regimes by multiplying the odds of treatment, offering a flexible framework for causal effect estimation. To study incremental effects when there are unobserved confounders, we adopt Rosenbaum's sensitivity model in single time point settings, and propose a doubly robust estimator for the resulting effect bounds. The bound estimators are asymptotically normal under mild conditions on nuisance function estimation. We show that incremental effect bounds can be narrower or wider than those for mean potential outcomes, and that the bounds must lie between the expected minimum and maximum of the conditional bounds on E(Y^0|X) and E(Y^1|X). For time-varying treatments, we consider the marginal sensitivity model. Although sharp bounds for incremental effects are identifiable from longitudinal data under this model, practical estimators have not yet been established; we discuss this challenge and provide partial results toward implementation. Finally, we apply our methods to study the effect of victimization on subsequent offending using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), illustrating the robustness of our findings in an empirical setting.

翻译：在增量倾向得分干预下，针对未测量混杂因素的敏感性分析研究仍相对不足。增量干预通过乘数调整处理几率来定义随机处理机制，为因果效应估计提供了灵活框架。为研究存在未观测混杂因素时的增量效应，我们在单时间点设定中采用罗森鲍姆敏感性模型，并提出针对所得效应边界的双重稳健估计量。在辅助函数估计满足温和条件下，该边界估计量具有渐近正态性。我们证明增量效应边界可能比均值潜在结果的边界更窄或更宽，且该边界必须位于条件边界E(Y^0|X)和E(Y^1|X)的期望最小值与最大值之间。针对时变处理，我们考虑边际敏感性模型。虽然在该模型下增量效应的尖锐边界可从纵向数据中识别，但实用估计量尚未建立；我们讨论了这一挑战并提供了面向实施的部分结果。最后，我们将所提方法应用于国家青少年至成人健康纵向研究（Add Health）数据，以研究受害经历对后续犯罪行为的影响，在实证场景中展示了研究结果的稳健性。