Toolboxes & Code Repositories

Causalteshap

Library for treatment effect analysis

software
Description:

causalteshap is a treatment effect analysis method that uses statistical hypothesis testing on Shapley values to find predictive and prognostic features.

More Information:

  • Originates from the AI-Driven Data Science research challenge (FAIR 2019–2023).

  • Responsible Research Lead:Sofie Van Hoecke

  • Authors: Jarne Verhaeghe, Femke Ongenae, Sofie Van Hoecke

  • Programming Language: Python

  • Link to the repository

  • Categories: supervised learning

Publications:

  • J. Verhaeghe, F. Ongenae, and S. Van Hoecke, “Causalteshap : discerning predictive from prognostic features for treatment effect analysis,” INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, vol. 16, pp. 7487–7507, 2025.