Carlson

<p>This data set gives the outcomes a well as treatment assignments of the
conjoint analysis in Carlson (2015). Please Carlson (2015) and Egami and
Imai (2019) for more details.</p>

datasets

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).
It contains a variety of regularization techniques to facilitate
analysis of large factorial experiments.

Naoki Egami

FindIt

Finding Heterogeneous Treatment Effects

Marc Ratkovic

Kosuke Imai

Carlson function

<p>A data frame consisting of 7 columns (including a treatment
assignment vector) and 3232 observations.</p>
<table class="table">
<tr><td>outcome</td><td>integer</td><td>whether a profile is chosen</td><td>0,1</td></tr>
<tr><td>newRecordF</td><td>factor</td><td>record as a politician</td><td>7 levels</td></tr>
<tr><td>promise</td><td>factor</td><td>platform</td><td>3 levels (job, clinic, education)</td></tr>
<tr><td>coeth_voting</td><td>factor</td><td>whether a profile is coethnic to a respodent</td><td>Yes, No</td></tr>
<tr><td>Degree</td><td>factor</td><td>job whether a profile has relevant
degrees</td><td>4 Yes, No</td></tr>
</table><p></p><p></p>

Format

Data from conjoint analysis in Carlson (2015). — Carlson

<p>A data frame consisting of 7 columns (including a treatment
assignment vector) and 3232 observations.</p>
<table class='table'>
<tr><td>outcome</td><td>integer</td><td>whether a profile is chosen</td><td>0,1</td></tr>
<tr><td>newRecordF</td><td>factor</td><td>record as a politician</td><td>7 levels</td></tr>
<tr><td>promise</td><td>factor</td><td>platform</td><td>3 levels (job, clinic, education)</td></tr>
<tr><td>coeth_voting</td><td>factor</td><td>whether a profile is coethnic to a respodent</td><td>Yes, No</td></tr>
<tr><td>Degree</td><td>factor</td><td>job whether a profile has relevant
degrees</td><td>4 Yes, No</td></tr>
</table>



Carlson: Data from conjoint analysis in Carlson (2015).

Description

Arguments

Format

References

outcome	integer	whether a profile is chosen	0,1
newRecordF	factor	record as a politician	7 levels
promise	factor	platform	3 levels (job, clinic, education)
coeth_voting	factor	whether a profile is coethnic to a respodent	Yes, No
Degree	factor	job whether a profile has relevant degrees	4 Yes, No