Causal forests model treatment effect inhomogeneity. We use XAI tools to interpret such model to see which features are associated with the treatment effect.
This question sends shivers down the poor modelers spine… The {hstats} R package introduced in our last post measures their strength using Friedman’s H-statistics, a collection of statistics based on partial dependence functions. On Github, the preview version of {hstats} 1.0.0 out – I will try to bring it to CRAN ...
What makes a ML model a black-box? It is the interactions. Without any interactions, the ML model is additive and can be exactly described. Studying interaction effects of ML models is challenging. The main XAI approaches are: This post is mainly about the third approach. Its beauty is that we ...
It must have been around the year 2000, when I wrote my first snipped of SPLUS/R code. One thing I've learned back then: Loops are slow. Replace them with vectorized calculations or if vectorization is not possible, use sapply() et al. Since then, the R core team and the community ...