Package: FindIt
Version: 0.2
Date: 2013-01-25
Title: R Package for Finding Heterogeneous Treatment Effects
Author: Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
<kimai@princeton.edu>,
Maintainer: Marc Ratkovic <ratkovic@princeton.edu>
Depends: R (>= 2.15.0), glmnet, lars
Imports: glmnet, lars
Description: FindIt is an R package that implements the heterogeneous
treatment effect estimation procedure proposed by Imai and
Ratkovic (2013).  The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Packaged: 2013-01-25 01:19:39 UTC; ratkovic
Repository: CRAN
Date/Publication: 2013-01-25 07:53:46

Package: FindIt
Version: 0.3
Date: 2013-01-31
Title: R Package for Finding Heterogeneous Treatment Effects
Author: Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
<kimai@princeton.edu>,
Maintainer: Marc Ratkovic <ratkovic@princeton.edu>
Depends: R (>= 2.15.0), glmnet, lars
Imports: glmnet, lars
Description: FindIt is an R package that implements the heterogeneous
treatment effect estimation procedure proposed by Imai and
Ratkovic (2013).  The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Packaged: 2013-02-01 03:41:40 UTC; ratkovic
Repository: CRAN
Date/Publication: 2013-02-01 07:53:25