LaLonde

<p>This data set gives the outcomes a well as treatment assignments and
covariates for the National Supported Work Study, as analyzed in LaLonde
(1986).</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)<DOI:10.1080/01621459.2018.1476246>.
It contains a variety of regularization techniques to facilitate
analysis of large factorial experiments.

Naoki Egami

FindIt

Finding Heterogeneous Treatment Effects

LaLonde function

<p>A data frame consisting of 12 columns (including a treatment
assignment vector) and 2787 observations.</p><table><tr><td> outcome </td>
<td> integer </td>
<td> whether earnings in 1978 are
larger than in 1975 </td>
<td> 0,1 </td>
</tr><tr><td> treat </td>
<td> integer </td>
<td> whether the
individual received the treatment </td>
<td> 0,1 </td>
</tr><tr><td> age </td>
<td> numeric </td>
<td> age in
years </td>
<td> </td>
</tr><tr><td> educ </td>
<td> numeric </td>
<td> education in years </td>
<td> </td>
</tr><tr><td> black </td>
<td>
factor </td>
<td> black or not </td>
<td> 0,1 </td>
</tr><tr><td> hisp </td>
<td> factor </td>
<td> hispanic or not
</td>
<td> 0,1 </td>
</tr><tr><td> white </td>
<td> factor </td>
<td> white or not </td>
<td> 0,1 </td>
</tr><tr><td> marr </td>
<td>
factor </td>
<td> married or not </td>
<td> 0,1 </td>
</tr><tr><td> nodegr </td>
<td> factor </td>
<td> an indicator
for no high school degree </td>
<td> 0,1 </td>
</tr><tr><td> log.re75 </td>
<td> numeric </td>
<td> log of
earnings in 1975 </td>
<td> </td>
</tr><tr><td> u75 </td>
<td> factor </td>
<td> unemployed in 1975 </td>
<td> 0,1
</td>
</tr></table>

LaLonde: National Supported Work Study Experimental Data

Description

Arguments

Format

References

outcome	integer	whether earnings in 1978 are larger than in 1975	0,1
treat	integer	whether the individual received the treatment	0,1
age	numeric	age in years
educ	numeric	education in years
black	factor	black or not	0,1
hisp	factor	hispanic or not	0,1
white	factor	white or not	0,1
marr	factor	married or not	0,1
nodegr	factor	an indicator for no high school degree	0,1
log.re75	numeric	log of earnings in 1975
u75	factor	unemployed in 1975	0,1