FindIt-package

CreateWeights

CreatelevelIndex

lengthSlack

Lcombinefunction

PsyConstraintCombine

CreateANOVAconst

Zcombinefunction

Glsei

CoefExtract

rangeCausalANOVAFit

CausalANOVAFit

stab.CausalANOVA

ScreenINT

glinternet.cv

indTwo2Three

NoRegularization

Collapsing

FullVCOV

cluster_se_glm

makeallway

maketwoway

<table><tr><td> Package: </td>
<td> factorEx</td>
</tr><tr><td> Type: </td>
<td> Package</td>
</tr><tr><td> Version: </td>
<td> 1.1.5</td>
</tr><tr><td>
Date: </td>
<td> 2019-11-19</td>
</tr></table>

package

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 (2019)<DOI:10.1080/01621459.2018.1476246>.
It contains a variety of regularization techniques to facilitate
analysis of large factorial experiments.

FindIt-package: FindIt: Finding Heterogeneous Treatment Effects

Description

Arguments

References

Package:	factorEx
Type:	Package
Version:	1.1.5
Date:	2019-11-19