Causal Foundation Models
A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.
Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.
- CFMs estimate causal quantities like average treatment effect without per-problem pipelines
- Positioned as an emerging paradigm applying foundation models to causal inference
From the original article
1 min read at huggingface.co
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.
By Christopher Stith, Hossein Rahmani, Jesse C. Cresswell · huggingface.co
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