首个面向连续处理的因果基础模型，无需微调即可泛化到新任务。
Causal Foundation Models with Continuous Treatments
- 构建连续处理的生成先验，合成丰富因果训练数据。
- 仅用观测数据重建个体处理-响应曲线，性能超越专用模型。
- 适合需要快速适应新因果任务的研究者与应用开发者。
因果推断是从观察数据中估计因果效应的基础工具，尤其在连续处理设置下尤为重要——干预变量具有连续取值范围。该场景远未被充分探索，相比二元处理情形，模型需在连续处理值上表征效应。本文首次提出面向连续处理设置的因果基础模型。该模型通过元学习，在无需额外训练或微调的前提下，可预测多种未见任务中的因果效应。我们设计了一种新型的数据生成过程先验，用于生成包含连续处理变量的丰富因果训练语料。随后，利用Transformer模型仅凭观测数据重建个体处理-响应曲线，并借助上下文学习来近似昂贵的贝叶斯后验推断。实验表明，该模型在个体处理-响应曲线重建任务上优于专门为此类任务训练的因果模型。推理代码（含训练权重）可在 https://github.com/layer6ai-labs/CCPFN-inference 获取。
原文摘要 · Abstract (English)
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. Inference code (including trained model weights) can be found at https://github.com/layer6ai-labs/CCPFN-inference .
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