Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search
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Abstract (arXiv)
We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 1 of its 22 references that are in the map. First posted on arXiv 13 Aug 2023.
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1 of 22 references.
References · 1
- 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 63
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Citing papers per year: 2024: 1; 2025: 5; 2026: 2.
8 papers.
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- 2025Causal Discovery for Linear Non-Gaussian Models with Disjoint Cyclescited by 3
- 2026Dynamic Expert-Guided Model Averaging for Causal Discoverycited by 2
- 2026Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Datacited by 1
- 2025An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contextscited by 0
- 2025Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"cited by 0
- 2026Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data7 May 2026
- 2026Dynamic Expert-Guided Model Averaging for Causal Discovery23 Jan 2026
- 2025Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"16 Oct 2025
- 2025Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles14 Jul 2025
- 2025An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts19 Mar 2025
- 2025Causally Reliable Concept Bottleneck Models6 Mar 2025
- 2025SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph11 Feb 2025
- 2024CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research21 May 2024