Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation. Here, we instead present an in-context learning approach. Specifically, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks. A key challenge is that the sensitivity bounds are not directly available when sampling training data. To address this, we develop a general prior-data construction that is applicable across the class of generalized treatment sensitivity models. Our construction involves a Lagrangian scalarization of the objective to generate training labels for the bounds through a tradeoff between causal effect min/max-imization and sensitivity model violation, which avoids model-specific analytical derivations. We further show that, under standard convexity and linearity conditions, our objective recovers the full Pareto frontier of solutions. Empirically, we demonstrate our amortized approach across various datasets, causal queries, and sensitivity levels, where our approach achieves a test-time computation that is orders of magnitude faster than per-instance methods. To the best of our knowledge, ours is the first foundation model for in-context learning for causal sensitivity analysis.

翻译：因果敏感性分析旨在存在未观测混杂因素时提供因果效应估计的界限。然而，现有的因果敏感性分析方法是逐实例过程，这意味着对数据集、因果查询、敏感性水平或处理的更改都需要重新计算。本文提出了一种情境学习方法。具体而言，我们基于先验数据拟合网络提出了一种用于因果敏感性分析的摊销方法。一个关键挑战是，在采样训练数据时无法直接获取敏感性界限。为解决这一问题，我们开发了一种通用的先验数据构造方法，该方法适用于广义处理敏感性模型类别。我们的构造涉及对目标进行拉格朗日标量化，通过在因果效应最小化/最大化与敏感性模型违反之间的权衡生成训练标签的界限，从而避免了特定于模型的解析推导。我们进一步证明，在标准凸性和线性条件下，我们的目标函数能够恢复完整的帕累托解前沿。实验表明，我们的摊销方法在各种数据集、因果查询和敏感性水平下均有效，其测试时间计算速度比逐实例方法快数个数量级。据我们所知，这是首个用于因果敏感性分析情境学习的基础模型。