A1448
Title: Inference on local variable importance measures for heterogeneous treatment effects
Authors: Pawel Morzywolek - University of Copenhagen (Denmark) [presenting]
Peter Gilbert - University of Washington and Fred Hutchinson Cancer Research Center (United States)
Alex Luedtke - University of Washington (United States)
Abstract: An inferential framework is provided 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. The 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.
