Heterogeneous treatment effect analysis based on machine-learning methodology.

ABSTRACT: Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine-learning algorithm. We conducted a systematic performance e

SUBMITTER: Gong X 

PROVIDER: S-EPMC8592515 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature