2023/08/13 by Joseph Ramsey, Ramsey, Joseph D., Bryan Andrews +1 · 1 citation
Computer Science · Mathematics · #62D20 #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #D.1.m #FOS: Computer and information sciences #I.2.5 #Mathematical Software (cs.MS) #Programming Languages (cs.PL) #Statistical Methods and Bayesian Inference
paper · doi:10.48550/arxiv.2308.07346
openalex publication_date 2023/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.