A Note on the Use of Recursive Partitioning in Causal Inference

被引：0

作者：

Conversano, Claudio ^{[1
]}

Cannas, Massimo ^{[2
]}

Mola, Francesco ^{[2
]}

机构：

[1] Dept Math & Informat, Via Osped 72, I-09124 Cagliari, Italy

[2] Univ Cagliari, Dipartimento Sci Econ & Aziendali, I-09123 Cagliari, Italy

来源：

Advances in Statistical Models for Data Analysis | 2015年

关键词：

Average treatment effect; Balancing recursive partitioning; Regression trees; Resampling;

D O I：

10.1007/978-3-319-17377-1_7

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A tree-based approach for identification of a balanced group of observations in causal inference studies is presented. The method uses an algorithm based on a multidimensional balance measure criterion applied to the values of the covariates to recursively split the data. Starting from an ad-hoc resampling scheme, observations are finally partitioned in subsets characterized by different degrees of homogeneity, and causal inference is carried out on the most homogeneous subgroups.

引用

页码：55 / 62

页数：8

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