让机器学习模型像用大模型一样做因果推断，无需重新训练。
Causal Foundation Models
- 用预训练神经网络实现因果推断，通过上下文学习直接应用新数据。
- 在不更新模型的前提下，可对新数据集估计平均处理效应等因果量。
- 适合想快速部署因果分析但缺乏统计背景的研究者和工程师。
因果推断是从数据中估计干预或处理效果的实践。传统方法需为每个新问题定制流程：先提出因果机制，再选择适配的估计器，最后进行训练。与此同时，机器学习领域已转向基础模型范式——一次大规模预训练后，即可在新任务上无须微调地应用。因果基础模型（CFMs）将此范式引入因果推断。CFMs 是预训练的神经网络，可通过上下文学习，在全新数据集上估计平均处理效应等因果量，且无需模型更新。本文提供该新兴领域的实用入门指南，梳理因果推断与机器学习的基础知识，并讨论CFMs的应用。文中包含示例代码和Jupyter笔记本。
原文摘要 · Abstract (English)
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs) bring this paradigm to causal inference. CFMs are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates. This work provides a practical introduction to this emerging area. We summarize the necessary background in causal inference and machine learning before discussing CFMs. Throughout, we include example code and Jupyter notebooks.
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