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
pretreatment 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.

Author  Naoki Egami <[email protected]>, Marc Ratkovic <[email protected]>, Kosuke Imai <[email protected]>, 
Date of publication  20180121 18:33:07 UTC 
Maintainer  Naoki Egami <[email protected]> 
License  GPL (>= 2) 
Version  1.1.3 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("kosukeimai/FindIt")

kosukeimai/FindIt documentation built on Jan. 25, 2018, 10:26 a.m.