vdblm/CausalPFN
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
This project helps data scientists, machine learning engineers, and researchers quickly understand how different actions (treatments) truly impact outcomes. It takes your observational data, including features, treatments, and outcomes, and efficiently estimates both individual-level (CATE) and average-level (ATE) causal effects, even quantifying their uncertainty. The output helps you make more informed, personalized decisions.
Use this if you need to rapidly estimate causal effects from various datasets without retraining a model for each new scenario, especially in applications like personalized marketing or A/B testing analysis.
Not ideal if you primarily work with very small datasets or require highly interpretable, simple causal models that don't rely on complex deep learning architectures.
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91
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11
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Feb 27, 2026
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