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 NeuralCSA 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，使得从观察性数据进行因果推断具有挑战性。作为一种补救措施，因果敏感性分析是在未观测混杂下以数学保证得出因果结论的重要工具。本文提出NeuralCSA，一个用于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：（i）一大类敏感性模型，包括边际敏感性模型、f-敏感性模型和Rosenbaum敏感性模型；（ii）不同类型的处理（即二元和连续处理）；以及（iii）不同类型的因果查询，包括（条件）平均处理效应和对多个结果的联合效应。NeuralCSA的通用性是通过学习与处理干预相对应的潜在分布偏移来实现的，该偏移使用两个条件归一化流。我们提供了理论保证，证明NeuralCSA能够推断出感兴趣因果查询的有效边界，并通过模拟数据和真实数据在实证上证实了这一点。