/Causality-in-Churn

Code for the article "Does causal reasoning help preventing churn?"

Primary LanguageR

Causality in Churn

Code for the article "Does causal reasoning help preventing churn?", co-authored by Théo Verhelst, Olivier Caelen, Jean-Christophe Dewitte, and Gianluca Bontempi.

Content

  • Implementation of the true and estimated uplift curve. The true uplift curve can be used in simulation settings, whereas its estimated counterpart can be used in empirical settings.
  • Implementation of the causal precision curve (see article).
  • Hierarchical bayesian generative model of customer churn, which can be parametrized to simulate any distribution of customer types
  • Example of use.

Requirements

  • R (>= 3.4.1)
  • ggplot2 for the examples