Title: An optimal cluster-based approach for Subgroup Analysis using Information Complexity Criterion

Authors: Ida D'Attoma; Caterina Liberati

Addresses: Statistics Department, Universitá di Bologna, Via Belle Arti n 41, 40121 Bologna, Italy. ' Economics Department, Universitá degli Studi Milano-Bicocca, P.zza Ateneo Nuovo n 1, 20126 Milano, Italy

Abstract: Subgroup Analysis (SA) is a helpful technique in the context of randomised experiments and in observational studies. With reference to program evaluation, it helps in determining whether and how treatment effects vary across subgroups induced by baseline covariates. However, the choice of the optimal number of subgroups is often ambiguous and causes concern. Here, SA is conducted using the cluster-based approach introduced in D'Attoma and Camillo (2011) and the usage of the Information Complexity Criterion to select the optimal number of groups is proposed. A simulation study and a real case have been illustrated to show such promising approach.

Keywords: heterogeneous treatment effects; subgroup analysis; information complexity criterion; observational studies; simulation; clustering.

DOI: 10.1504/IJBIDM.2011.044978

International Journal of Business Intelligence and Data Mining, 2011 Vol.6 No.4, pp.402 - 425

Received: 04 Jun 2011
Accepted: 21 Oct 2011
Published online: 22 Apr 2015 *

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