# About

Tetrad is an open-source platform for causal discovery, simulation, and causal
model analysis developed at **Carnegie Mellon University** and the **University
of Pittsburgh**. Originating in the philosophy of science and machine learning
communities, the project has grown over several decades into a comprehensive
environment for learning, evaluating, and reasoning with graphical causal
models.

This manual provides consolidated, up-to-date documentation for Tetrad’s
algorithms, graphical model representations, data formats, effect-estimation
tools, and user interfaces. It replaces and expands the older “classic” HTML
manual, incorporating modern algorithms, updated descriptions, and extensive
cross-referencing.

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## 📚 Project Background

Tetrad supports a full ecosystem of causal modeling tools, including:

- DAG, CPDAG, MAG, and PAG causal graphs
- constraint-based, score-based, and hybrid search algorithms
- methods for learning with latent variables and selection bias
- effect-size estimation, adjustment sets, and path blocking
- nonlinear and non-Gaussian independence tests and scores
- simulation frameworks for linear, nonlinear, and neural-network–based SEMs
- extensive GUI tools and programmatic interfaces

In addition to the Java/GUI version, Tetrad includes modern APIs:

- **Py-Tetrad** — Python interface (with its own 2023 software paper)
- **RPy-Tetrad** — R interface via rpy2
- The core **Java library**, used both in the GUI and headless workflows.

Development is ongoing, with new algorithms and methods added as time and
resources allow. Community suggestions and contributions are welcome.

For a broader historical overview of the project, see:

👉 **[https://www.cmu.edu/dietrich/philosophy/tetrad/about/](https://www.cmu.edu/dietrich/philosophy/tetrad/about/)**

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## 👥 Contributors

Tetrad has benefited from the work of many collaborators, including developers,
research scientists, graduate students, and external contributors.

A maintained list appears here:

👉 **[Contributors](contributors.md)**

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## 📄 Papers and Books

A curated bibliography of foundational publications, software papers,
theoretical works, and algorithm references relevant to Tetrad and its tools:

👉 **[Papers and Books](papers-and-books.md)**

This list includes classic references alongside modern work on hybrid,
nonlinear, and high-dimensional causal discovery.

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## 📬 Questions or Suggestions?

Suggestions for improving the manual are welcome.  
Please open an issue or discussion on our GitHub repository:

👉 **https://github.com/cmu-phil/tetrad-manual-rtd**