FindIt (version 0.5)

Immigration: Data from conjoint analysis in Hainmueller and Hopkins (2014) and Hainmueller, Hopkins and Yamamoto (2014).

Description

This data set gives the outcomes a well as treatment assignments the conjoint analysis in Hainmueller and Hopkins (2014) and Hainmueller, Hopkins and Yamamoto (2014).

Usage

data

Format

A data frame consisting of 6 columns and 6980 observations (5 profiles for each 1396 respondents).

outcome	integer	whether a profile is chosen
0,1	Education	factor
education	7 levels	Gender
factor	gender	male or female
Origin	factor	origin
10 levels	Experience	factor
job experience	4 levels	outcome

Source

Data from the conjoint analysis in Hainmueller and Hopkins (2014) and Hainmueller, Hopkins and Yamamoto (2014). Because of pairings, we randomly select one profile within each pair and define a binary outcome variable for that pair, which is equal to 1 if this profile is chosen and to 0 if the other profile is selected. Columns contain 5 factors from the original conjoint analysis. The details of levels of each factor are described in Hainmueller and Hopkins (2014) and Egami and Imai (2015).

References

Hainmueller, J. and Hopkins, D. J. 2014. ``The hidden american immigration consensus: A conjoint analysis of attitudes toward immigrants.'' American Journal of Political Science Forthcoming.

Hainmueller, J., Hopkins, D. J., and Yamamoto, T. 2014. ``Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments.'' Political Analysis, Vol.22, No.1, pp. 1-30.

Imai, Kosuke and Marc Ratkovic. 2013. ``Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation.'' Annals of Applied Statistics, Vol.7, No.1(March), pp. 443-470. http://imai.princeton.edu/research/files/svm.pdf

Egami, Naoki and Kosuke Imai. 2015. ``Causal Interaction in High-Dimension.'' Working paper. http://imai.princeton.edu/research/files/int.pdf

Examples

################################################### 
## Conjoint Analysis: Causal Interaction.
################################################### 
data(Immigration)

## Not run: 
# ## The SVM classifier with a lasso constraint is estimated.
# ## The model includes all three-way and two-way interactions as 
# ## well as main effects of five factorial treatments.
# 
# ## Run to search for lambdas.
# F.conjoint<- FindIt(model.treat= outcome ~
#                     Education+Gender+Origin+Experience+Plans,
#                     nway=3,
#                     data = Immigration,
#                     type="binary",
#                     treat.type="multiple")
# 
# ## Make the full factorial design matrix as the target population. 
# full <- full.FindIt(F.conjoint)
# 
# ## Compute the predicted potential outcomes for the target population.
# Unifdata <- predict(F.conjoint,newdata=full,sort=FALSE)$data
# ## End(Not run)
## load pre-computed F.conjoint and Unifdata
data(F.conjoint)
data(Unifdata)

## Compute AMTEs, ATCEs, and AMTIEs

## Range of each factor interaction.
## The range of the AMTEs for all factors
compare1 <- INT(F.conjoint,target.data=Unifdata,compare=TRUE,order=1)
compare1

## The range of the two-way AMTIEs for all two-way factor interactions
compare2 <- INT(F.conjoint,target.data=Unifdata,compare=TRUE,order=2)
compare2

## Not run: 
# ## The range of the three-way AMTIEs for all three-way factor interactions
# compare3 <- INT(F.conjoint,target.data=Unifdata,compare=TRUE,order=3)
# compare3
# ## End(Not run)

## Compute AMTIEs within factor interactions.
## Origin x Experience
out.OP <- INT(F.conjoint,target.data=Unifdata,
              column=c("Origin","Experience"),
              base=c("India","No.job"), order=2)
out.OP

## Not run: 
# ## Education x Gender x Origin
# out.EGO <- INT(F.conjoint,target.data=Unifdata,
#                column=c("Education","Gender","Origin"),
#                base=c("No.formal","female","India"), order=3)
# out.EGO
# ## End(Not run)