Problem
Research questions and friction points this paper is trying to address.
Causal Inference
Foundation Models
In-Context Learning
Causal Foundation Models
🤖 AI Summary
该研究通过预训练神经网络（因果基础模型CFMs）在不更新模型的情况下估计新的数据集中的因果量，如平均治疗效果，来解决传统因果推理需要为每个新问题定制流程的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal Foundation Models
in-context learning
pretrained neural networks
average treatment effect
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