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13 search results found

Dowhy ⭐ 6,730

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Causalnex ⭐ 2,109

A Python library that helps data scientists to infer causation rather than observing correlation.

Causaldiscoverytoolbox ⭐ 804

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Causallib ⭐ 653

A Python package for modular causal inference analysis and model evaluations

Bnlearn ⭐ 406

Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

Causal Ml ⭐ 376

Must-read papers and resources related to causal inference and machine (deep) learning

Python package for causal discovery based on LiNGAM.

A resource list for causality in statistics, data science and physics

Awesome Graph Causal Learning ⭐ 108

A list of Graph Causal Learning materials.

Benchpress ⭐ 52

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

Causal Semantic Generative Model ⭐ 43

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Dynamic causal Bayesian optimisation

Graphical_model_learning ⭐ 5

Learning graphical models, with a focus on causal models and learning from interventional data.

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1-13 of 13 search results

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