Speaker: Heidi Seibold, LMU - University of Munich (GER)

Via ZOOM -  https://uio.zoom.us/j/66792241824.

Title: Model-based trees and random forests for personalized 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. The similarity measure can then be used to estimate personalised models. The R package model4you implements these stratified and personalised models 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

Contact Information:
Riccardo De Bin – debin@math.uio.no
Riccardo Parviero – riccarpa@math.uio.no

Heidi Seibold is a post-doc in the working group for Computational Statistics at LMU Munich (GER)