Python and R Bindings

Tetrad includes an extensive Java implementation of causal discovery algorithms, scores, tests, data structures, and utilities.
To make this functionality easier to use from other programming environments, we provide official Python and R bindings that directly interface with the Java core.

These bindings allow researchers to access the full power of the Java Tetrad engine while working in Python or R.

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py-tetrad (Python Binding)

Repository:
👉 https://github.com/cmu-phil/py-tetrad

py-tetrad is the official Python interface to the Tetrad Java codebase. It exposes:

• Full access to Tetrad causal discovery algorithms (PC, FCI, GFCI, BOSS, GRaSP, etc.)

• Scores, independence tests, and utilities

• Graph objects and graph manipulation APIs

• Dense and sparse matrix tools

• Simulation modules

• Seamless conversion between Python data structures (NumPy arrays, pandas DataFrames) and Tetrad DataSets

py-tetrad is ideal when you want to:

• Use Tetrad’s Java algorithms from Python

• Access algorithms not included in native-Python packages

• Ensure feature parity with Tetrad GUI / Java library

• Run algorithms at Java-level performance, especially for large datasets

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rpy-tetrad (R Binding)

Repository:
👉 https://github.com/cmu-phil/py-tetrad/tree/main/pytetrad/R

rpy-tetrad provides an R interface built on the py-tetrad infrastructure. It lets R users:

• Call Tetrad Java search algorithms directly from R

• Pass R data frames into the Java Tetrad backend

• Retrieve graphs, adjustment sets, statistics, and scores into R

• Script large-scale analyses using familiar R workflows

rpy-tetrad is recommended if you:

• Work primarily in R but want direct access to Tetrad’s Java engine

• Need methods not implemented in R packages such as pcalg, dagitty, or bnlearn

• Want consistent results across GUI, Java, Python, and R contexts

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When to Use These Bindings

Use py-tetrad or rpy-tetrad when:

• You need exact behavior from Tetrad’s Java implementation
  (e.g., FGES, FCI variants, BOSS, GRaSP, FCIT, BF-BIC, BF-LRT, O-sets, RA, etc.)

• You want algorithms or features not implemented natively in Python or R

• You want to call Tetrad from another language but retain:

  • Same parameters

  • Same scoring behavior

  • Same PAG/CPDAG semantics

  • Same orientation rules

  • Same simulation procedures

In short:
If your work depends on the Tetrad Java engine, these bindings are the intended way to access it.

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Related Python Ecosystem Tools

The Python ecosystem also includes the causal-learn project, which implements several graph-based causal discovery algorithms natively in Python:

👉 https://causal-learn.readthedocs.io/en/latest/

Relationship to Tetrad

• causal-learn provides well-maintained Python-native implementations of several Tetrad algorithms, plus additional methods and utilities not included in Tetrad.

• py-tetrad provides a bridge to Tetrad’s full Java engine, including many algorithms and features not currently implemented anywhere else in Python.

Recommendation

• If you need pure-Python implementations with good ecosystem integration, causal-learn is a great choice.

• If you need the complete Tetrad functionality (including the newest research algorithms), use py-tetrad or rpy-tetrad.

These projects complement each other well, and many users employ both.

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