This paper presents a data driven approach that enables one to obtain a global measure of imbalance and to test it in a multivariate way. The main idea is based on the general framework of Partial Dependence Analysis (Daudin, 1981 J. J.) and thus of Conditional Multiple Correspondences Analysis (Escofier, B. (1988). Analyse des correpondances multiples conditionelle. La Revue de Modulad) as tools for investigating the dependence relationship between a set of observed categorical covariates (X) and an assignment-to-treatment indicator variable (T), in order to obtain a global imbalance measure (GI) according to their dependence structure.We propose the use of suchmeasure within a strategy whose aimis to compute treatment effects by subgroups. A toy example is presented for illustrate the performance of this promising approach.

Assessing Balance of Categorical Covariates and Measuring Local Effects in Observational Studies.

CAMILLO, FURIO;D'ATTOMA, IDA
2011

Abstract

This paper presents a data driven approach that enables one to obtain a global measure of imbalance and to test it in a multivariate way. The main idea is based on the general framework of Partial Dependence Analysis (Daudin, 1981 J. J.) and thus of Conditional Multiple Correspondences Analysis (Escofier, B. (1988). Analyse des correpondances multiples conditionelle. La Revue de Modulad) as tools for investigating the dependence relationship between a set of observed categorical covariates (X) and an assignment-to-treatment indicator variable (T), in order to obtain a global imbalance measure (GI) according to their dependence structure.We propose the use of suchmeasure within a strategy whose aimis to compute treatment effects by subgroups. A toy example is presented for illustrate the performance of this promising approach.
New Perspectives in Statistical Modeling and Data Analysis
465
472
F.Camillo; I.D'Attoma
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/130177
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