Causal inference from observational data is crucial for many disciplines such as medicine and economics. However, sharp bounds for causal effects under relaxations of the unconfoundedness assumption (causal sensitivity analysis) are subject to ongoing research. So far, works with sharp bounds are restricted to fairly simple settings (e.g., a single binary treatment). In this paper, we propose a unified framework for causal sensitivity analysis under unobserved confounding in various settings. For this, we propose a flexible generalization of the marginal sensitivity model (MSM) and then derive sharp bounds for a large class of causal effects. This includes (conditional) average treatment effects, effects for mediation analysis and path analysis, and distributional effects. Furthermore, our sensitivity model is applicable to discrete, continuous, and time-varying treatments. It allows us to interpret the partial identification problem under unobserved confounding as a distribution shift in the latent confounders while evaluating the causal effect of interest. In the special case of a single binary treatment, our bounds for (conditional) average treatment effects coincide with recent optimality results for causal sensitivity analysis. Finally, we propose a scalable algorithm to estimate our sharp bounds from observational data.

翻译：从观察数据中进行因果推断对医学和经济学等众多学科至关重要。然而，在放松无混杂假设（因果敏感性分析）条件下，因果效应的严格界限仍处于持续研究中。迄今为止，具有严格界限的研究仅限于相当简单的场景（例如，单个二元处理变量）。本文中，我们提出了一个适用于不同场景下存在未观测混杂时的因果敏感性分析统一框架。为此，我们提出了边际敏感性模型（MSM）的灵活推广，进而推导出广泛因果效应类别的严格界限。这包括（条件）平均处理效应、中介分析与路径分析的效应，以及分布效应。此外，我们的敏感性模型适用于离散、连续和时变处理变量。该模型使我们能够将未观测混杂下的部分识别问题解释为评估目标因果效应时潜在混杂变量的分布偏移。在单个二元处理变量的特殊情况下，我们关于（条件）平均处理效应的界限与最近因果敏感性分析的最优性结果一致。最后，我们提出了一种可扩展算法，用于从观察数据中估计我们的严格界限。