Package: FindIt
Version: 0.4
Date: 2015-02-02
Title: Finding Heterogeneous Treatment Effects
Author: Naoki Egami <naoki.egami5@gmail.com>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>,
Maintainer: Naoki Egami <naoki.egami5@gmail.com>
Depends: R (>= 2.15.0), glmnet, lars, Matrix
Description: FindIt 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)
Repository: CRAN
NeedsCompilation: no
Packaged: 2015-02-07 18:08:36 UTC; naokiegami
Date/Publication: 2015-02-08 00:31:16

Package: FindIt
Version: 0.5
Date: 2015-02-27
Title: Finding Heterogeneous Treatment Effects
Author: Naoki Egami <naoki.egami5@gmail.com>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>,
Maintainer: Naoki Egami <naoki.egami5@gmail.com>
Depends: R (>= 2.15.0), glmnet, lars, Matrix
Description: 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.
The package also contains the function, INT, which estimates
the average marginal treatment effect, the average treatment
combination effect, and the average marginal treatment interaction
effect proposed by Egami and Imai (2015).
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Repository: CRAN
Packaged: 2015-02-27 08:31:22 UTC; naokiegami
NeedsCompilation: no
Date/Publication: 2015-02-27 12:11:22