larslorch/avici
Amortized Inference for Causal Structure Learning, NeurIPS 2022
This tool helps researchers and data scientists understand underlying relationships by inferring causal structures from real-world data. You input a matrix of observations for different variables, and it outputs the probabilities of causal links between them. This is ideal for anyone working with observational data who needs to determine 'what causes what' rather than just 'what correlates with what'.
No commits in the last 6 months. Available on PyPI.
Use this if you need to discover causal relationships from observational data, especially when you have prior knowledge that can be described by a data-generating simulator.
Not ideal if your primary goal is simple correlation analysis or if you lack a clear understanding of the data generation process for your specific domain.
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Language
Python
License
MIT
Category
Last pushed
Feb 11, 2025
Commits (30d)
0
Dependencies
18
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