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May 29, 2026SoftwareX0 citationsOpen Access

CCI: An R package for computational conditional Independence testing

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CTChristian B. H. ThorjussenKLKristian Hovde LilandLSLars Erik Solberg

Key Points

  • The research aims to develop an R package for effective conditional independence testing in various data contexts.
  • Developed a model-agnostic R package called CCI for conditional independence testing.
  • Incorporated machine learning models and Monte Carlo cross-validation for robust error control.
  • Included features for automated hyperparameter tuning and visualization tools.
  • The CCI package effectively supports continuous, categorical, and mixed data structures.
  • Enhanced error control is achieved across multiple complex data scenarios.
  • Facilitates significant advancements in causal inference research and applied data analysis.

Abstract

The CCI package provides a computational framework for conditional independence testing in R, combining machine learning models with Monte Carlo cross-validation to deliver robust error control across complex data structures. CCI is model-agnostic, user-friendly, and supports continuous, categorical, and mixed data. Key functionalities include automated hyperparameter tuning, flexible direction selection, and visualization tools for null distributions and p-values. By lowering the barrier to rigorous conditional independence testing, the package facilitates advances in causal inference research and applied data analysis.

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Cite This Study

Thorjussen et al. (2026) studied this question.

synapsesocial.com/papers/6a192c67fab5b468c44153a9https://doi.org/10.1016/j.softx.2026.102726
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