model4you: An R Package for Personalised Treatment Effect Estimation

Abstract

Typical models estimating treatment effects assume that the treatment effect is the same for all individuals. Model-based recursive partitioning allows to relax this assumption and to estimate stratified treatment effects (model-based trees) or even personalised treatment effects (model-based forests). With model-based trees one can compute treatment effects for different strata of individuals. The strata are found in a data-driven fashion and depend on characteristics of the individuals. Model-based random forests allow for a similarity estimation between individuals in terms of model parameters (e.g. intercept and treatment effect). The similarity measure can then be used to estimate personalised models. The R package 'model4you' implements these stratified and personalised models in the setting with two randomly assigned treatments with a focus on ease of use and interpretability so that clinicians and other users can take the model they usually use for the estimation of the average treatment effect and with a few lines of code get a visualisation that is easy to understand and interpret. Funding statement: Heidi Seibold and Torsten Hothorn were financially supported by the Swiss National Science Foundation (grants 205321_163456 and IZSEZ0_177091).

Keywords

• personalised medicine
• subgroup analysis
• model-based recursive partitioning
• unbiased trees
• treatment effect
• random forest