This paper presents a data driven approach that enables one to obtain a measure of comparability between-groups in the presence of observational data. The main idea lies in the use of the general framework of conditional multiple correspondences analysis as a tool for investigating the dependence relationship between a set of observable categorical covariates X and an assignment-to-treatment indicator variable T, in order to obtain a global measure of comparability between-groups according to their dependence structure. Then, we propose a strategy that enables one to find treatment groups, directly comparable with respect to pre-treatment characteristics, on which estimate local causal effects.

A new data mining approach to estimate causal effects of policy interventions

CAMILLO, FURIO;D'ATTOMA, IDA
2010

Abstract

This paper presents a data driven approach that enables one to obtain a measure of comparability between-groups in the presence of observational data. The main idea lies in the use of the general framework of conditional multiple correspondences analysis as a tool for investigating the dependence relationship between a set of observable categorical covariates X and an assignment-to-treatment indicator variable T, in order to obtain a global measure of comparability between-groups according to their dependence structure. Then, we propose a strategy that enables one to find treatment groups, directly comparable with respect to pre-treatment characteristics, on which estimate local causal effects.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/82003
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