Statistical treatment choice: an application to active labour market programmes

Choosing among a number of available treatments the most suitable for a given subject is an issue of everyday concern. A physician has to choose an appropriate drug treatment or medical treatment for a given patient, based on a number of observed covariates X and prior experience. A case worker in an unemployment office has to choose among a variety of available active labour market programmes for unemployed job seekers. In this paper, two methodological advancements are developed: First, this methodology permits to combine a data set on previously treated individuals with a data set on new clients when the regressors available in these two data sets do not coincide. It thereby incorporates additional regressors on previously treated that are not available for the current clients. Such a situation often arises due to cost considerations, data confidentiality reasons or time delays in data availability. Second, statistical inference on the recommended treatment choice is analyzed and conveyed to the agent, physician or case worker in a comprehensible and transparent way. The implementation of this methodology in a pilot study in Switzerland for choosing among active labour market programmes (ALMP) for unemployed job seekers is described.