Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to draw causal conclusions under unobserved confounding with mathematical guarantees. In this paper, we propose NeuralCSA, a neural framework for generalized causal sensitivity analysis. Unlike previous work, our framework is compatible with (i) a large class of sensitivity models, including the marginal sensitivity model, f-sensitivity models, and Rosenbaum's sensitivity model; (ii) different treatment types (i.e., binary and continuous); and (iii) different causal queries, including (conditional) average treatment effects and simultaneous effects on multiple outcomes. The generality of \frameworkname is achieved by learning a latent distribution shift that corresponds to a treatment intervention using two conditional normalizing flows. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data.

翻译：未观测混杂在许多应用中普遍存在，使得基于观测数据进行因果推断面临挑战。作为应对策略，因果敏感性分析是在存在未观测混杂条件下，能够以数学保证得出因果结论的重要工具。本文提出NeuralCSA——一个面向广义因果敏感性分析的神经框架。与先前工作不同，我们的框架兼容以下特性：(i) 广泛类别的敏感性模型，包括边际敏感性模型、f-敏感性模型以及Rosenbaum敏感性模型；(ii) 不同处理类型（即二值型与连续型）；以及 (iii) 不同因果查询，包括（条件）平均处理效应及对多重结果的同步效应。该框架的通用性通过利用两个条件归一化流学习对应处理干预的潜在分布偏移来实现。我们提供了NeuralCSA能够推断目标因果查询有效边界的理论保证，并基于模拟数据与真实数据实证验证了其性能。