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

作者：Camillo F; D'Attoma Ida^*

来源：Expert Systems with Applications, 2010, 37(1): 171-181.

DOI：10.1016/j.eswa.2009.05.072

• Selection bias
• Program evaluation
• Data mining
• Conditional space
• Matrix decomposition

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.

• 出版日期2010-1