发表机构
Layer 6 AI; TD Bank Group（第六层人工智能公司; 道明银行集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究介绍将基础模型范式引入因果推断领域的因果基础模型（CFMs），说明其无需微调即可估计因果量的特性，并提供相关背景、示例代码与Jupyter笔记本的实用介绍。
AI 中文摘要
因果推断是从数据中估计处理或干预效应的实践，传统上针对每个新问题都需要定制化流程：首先提出因果机制，选择兼容的估计器，最后对其进行训练。与此同时，在各类场景和模态下，机器学习的大部分内容已转向基础模型范式：即大规模预训练一次，即可应用于新任务而无需微调。因果基础模型（Causal Foundation Models，简称CFMs）将这一范式引入因果推断领域。CFM是经过预训练的神经网络，可通过上下文学习在全新数据集上估计因果量（如平均处理效应），无需更新模型。本研究为这一新兴领域提供实用介绍，在讨论CFM前，我们总结了因果推断和机器学习的必要背景，全程包含示例代码和Jupyter笔记本。
英文摘要
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.
CommentsCode is available at https://github.com/layer6ai-labs/cfms
