IntelLabs/causality-lab
Causal discovery algorithms and tools for implementing new ones
This project helps researchers and practitioners understand cause-and-effect relationships from observed data. It takes your dataset and applies various algorithms to identify the underlying causal structure, even when some influencing factors are hidden or data comes in sequences like time series. The output is a causal graph, which visually represents how different variables impact each other, making it easier to reason about system behavior.
247 stars. No commits in the last 6 months.
Use this if you need to discover the causal links between variables in your dataset, especially when dealing with complex systems, latent variables, or time-series data.
Not ideal if you are looking for a simple, off-the-shelf prediction model or if your primary goal is correlation analysis rather than understanding causation.
Stars
247
Forks
30
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jul 10, 2025
Commits (30d)
0
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