MRC Biostatistics Unit seminar

Speaker: Pawel Morzywolek, University of Copenhagen
Series: MRC Biostatistics Unit Seminars
Admission (organiser's words): To register to attend remotely, please click here:
Note: This is a free online seminar. To register to attend remotely, please click here:
Also open online: the joining details are on the talk's page.

Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond

We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, where decision makers hesitate to rely on black-box treatment recommendation algorithms. The variable importance measures we consider are local in that they may differ across individuals, while the inference is global in that it tests whether a given variable is important for any individual. Our approach builds on recent developments in semiparametric theory for function-valued parameters, and is valid even when statistical machine learning algorithms are employed to quantify treatment effect heterogeneity. We demonstrate the applicability of our method to infectious disease prevention strategies.