Add to ORKGA tree-based method for identification of a balanced group of observa- tions in casual inference studies is presented. The method derives from an algorithm which uses a multidimensional balance measure criterion to recursively split the dataset based on the values of the covariates. Observations are finally partitioned in subsets characterized by different degrees of homogeneity. An ad-hoc resampling scheme is used to select the units for which causal inference can be carried out
<p>A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exp...
Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect o...
Abstract We have developed a recursive-partitioning (RP) algorithm for identifying phe...
A tree-based method for identification of a balanced group of observa- tions in casual inference stu...
A tree-based approach for identification of a balanced group of observations in causal inference stu...
A method is given which uses subject matter assumptions to discriminate recursive models and thus po...
Observational data are prevalent in many fields of research, and it is desirable to use this data to...
In causal inference a matching algorithm assigns a subset of control units to each treated unit. Usi...
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of...
The framework of this paper is supervised statistical learning in data mining. A typical data-mining...
The identification of patient subgroups with differential treatment effects is the first step toward...
The most basic approach to causal inference measures the response of a system or population to diffe...
<p>Fixed effects models are very flexible because they do not make assumptions on the distribution o...
his paper deals with the problem of dimension reduction in the general context of supervised stati...
An important goal in causal inference is to achieve balance in the covariates among the treatment gr...
<p>A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exp...
Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect o...
Abstract We have developed a recursive-partitioning (RP) algorithm for identifying phe...
A tree-based method for identification of a balanced group of observa- tions in casual inference stu...
A tree-based approach for identification of a balanced group of observations in causal inference stu...
A method is given which uses subject matter assumptions to discriminate recursive models and thus po...
Observational data are prevalent in many fields of research, and it is desirable to use this data to...
In causal inference a matching algorithm assigns a subset of control units to each treated unit. Usi...
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of...
The framework of this paper is supervised statistical learning in data mining. A typical data-mining...
The identification of patient subgroups with differential treatment effects is the first step toward...
The most basic approach to causal inference measures the response of a system or population to diffe...
<p>Fixed effects models are very flexible because they do not make assumptions on the distribution o...
his paper deals with the problem of dimension reduction in the general context of supervised stati...
An important goal in causal inference is to achieve balance in the covariates among the treatment gr...
<p>A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exp...
Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect o...
Abstract We have developed a recursive-partitioning (RP) algorithm for identifying phe...