Package: FindIt
Version: 1.0
Date: 2016-12-22
Title: Finding Heterogeneous Treatment Effects
Author: Naoki Egami <negami@princeton.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>,
Maintainer: Naoki Egami <negami@princeton.edu>
Depends: R (>= 3.1.0), arm
Imports: glmnet, lars, Matrix, quadprog, ggplot2, stats, graphics,
utils
Description: The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.
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, CausalANOVA, which estimates
the average marginal interaction effects by a regularized ANOVA as
proposed by Egami and Imai (2016+).
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Repository: CRAN
Packaged: 2016-12-31 00:56:29 UTC; negami
Date/Publication: 2016-12-31 08:42:19
NeedsCompilation: yes

Package: FindIt
Version: 1.1.1
Date: 2017-08-23
Title: Finding Heterogeneous Treatment Effects
Author: Naoki Egami <negami@princeton.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>,
Maintainer: Naoki Egami <negami@princeton.edu>
Depends: R (>= 3.1.0), arm
Imports: glmnet, lars, Matrix, quadprog, ggplot2, glinternet, igraph,
sandwich, lmtest, stats, graphics, utils
Description: The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.
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 a class of functions, CausalANOVA,
which estimates the average marginal interaction effects (AMIEs)
by a regularized ANOVA as proposed by Egami and Imai (2016+).
It contains a variety of regularization techniques to facilitate
analysis of large factorial experiments.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Repository: CRAN
Packaged: 2017-08-24 19:08:40 UTC; negami
Date/Publication: 2017-08-24 20:51:32 UTC
RoxygenNote: 6.0.1
NeedsCompilation: yes