Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variety of domains is the continuous treatment setting, where the variable of intervention has a continuous range. This setting is far less explored and represents a substantial shift from the binary treatment setting, with models needing to represent effects across a continuum of treatment values. In this paper, we present the first causal foundation model for the continuous treatment setting. Our model meta-learns the ability to predict causal effects across a wide variety of unseen tasks without additional training or fine-tuning. First, we design a novel prior over data-generating processes with continuous treatment variables in order to generate a rich causal training corpus. We then train a transformer to reconstruct individual treatment-response curves given only observational data, leveraging in-context learning to amortize expensive Bayesian posterior inference. Our model achieves state-of-the-art performance on individual treatment-response curve reconstruction tasks compared to causal models which are trained specifically for those tasks.

翻译：因果推断是从观测数据中估计因果效应的关键工具，广泛应用于多个学科领域。其中，连续处理变量场景（即干预变量具有连续取值范围）在众多领域中尤为重要。该场景的研究远少于二元处理变量场景，且存在本质差异——模型需要描述治疗值连续变化时的效应分布。本文首次提出面向连续处理变量的因果基础模型。该模型通过元学习机制，无需额外训练或微调即可预测各类未见任务的因果效应。首先，我们设计了包含连续处理变量的数据生成过程的新型先验分布，构建了丰富的因果训练语料库。随后，我们训练Transformer模型仅根据观测数据重建个体处理效应曲线，利用上下文学习摊销昂贵的贝叶斯后验推断过程。与专门为特定任务训练的因果模型相比，本模型在个体处理效应曲线重构任务中实现了最优性能。