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：从观测数据进行因果推断对于医学、经济学等许多学科至关重要。然而，在放宽无混杂假设条件下的因果效应紧界（因果敏感性分析）仍是持续研究的课题。目前，涉及紧界的研究局限于相当简单的设定（例如，单一二元处理变量）。本文提出一个统一框架，用于在存在未观测混杂的各种设定下进行因果敏感性分析。为此，我们首先提出边际敏感性模型（MSM）的灵活泛化形式，进而推导出一大类因果效应的紧界，包括（条件）平均处理效应、中介分析与路径分析中的效应以及分布效应。此外，我们的敏感性模型可适用于离散、连续及时变处理变量。该模型允许将未观测混杂下的部分识别问题解释为评估感兴趣因果效应时潜在混杂变量的分布偏移。在单一二元处理变量的特例中，我们关于（条件）平均处理效应的界与近期因果敏感性分析的最优性结果一致。最后，我们提出一种可扩展算法，用于从观测数据中估计所提出的紧界。