Assessing Treatment Effect Heterogeneity: Predictive Covariate Selection and Subgroup Identification

A common objective in a study that involves interventions is the evaluation of heterogeneity of the causal (or treatment) effect. Heterogeneity occurs when the effect varies in subsets of the data which may be defined by a few covariates/characteristics. The presence of heterogeneity can allow us to identify potentially promising interventions for different subsets. This project reviews methods for assessing the causal effect and focuses particularly on two closely related tasks: covariate selection and subgroup identification. The first task allows us to select a few covariates from a potentially large pool of candidates and focus on those that may explain the presence of treatment effect heterogeneity. The second allows us to identify subsets of the data with desirable characteristics, such as enhanced effects. In this project, we will study the properties of different algorithms for covariate selection, treatment effect estimation and subgroup identification. Based on the identified properties, we will explore possible extensions or suggest new approaches.