  This paper introduces X-SHAP, a model-agnostic method that assesses
multiplicative contributions of variables for both local and global
predictions. This method theoretically and operationally extends the so-called
additive SHAP approach. It proves useful underlying multiplicative interactions
of factors, typically arising in sectors where Generalized Linear Models are
traditionally used, such as in insurance or biology. We test the method on
various datasets and propose a set of techniques based on individual X-SHAP
contributions to build aggregated multiplicative contributions and to capture
multiplicative feature importance, that we compare to traditional techniques.
