Causal Foundation Models: Pretrained Nets Estimate Treatment Effects via In-Context Learning
chaumian · x · 2026-09-04
An arXiv paper introduces Causal Foundation Models (CFMs), bringing the foundation-model paradigm to causal inference. Instead of bespoke pipelines per problem, CFMs are pretrained neural networks that estimate causal quantities like average treatment effects on entirely new datasets via in-context learning, with no model updates. The paper is a practical introduction with background on causal inference and ML, plus example code and Jupyter notebooks.
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