fmeffects: An R Package for Forward Marginal Effects

Forward marginal effects (FMEs) (Scholbeck et al., 2022) provide simple yet accurate local modelagnostic explanations in terms of forward differences in prediction. They address questions of the form: If we change x by an amount h, what is the change in predicted outcome ŷ? For instance, given a medical study where a model is trained to predict a patient's disease risk, FMEs can tell us each patient's individual change in predicted risk due to losing 5kg in body weight. FMEs thus provide actionable and comprehensible advice for stakeholders, including ones without expertise in machine learning. If the change in predicted risk is substantial enough, doctors may recommend a tailored exercise and nutrition regimen.