Description
Quantile Treatment Effects.
Description
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
README.md
README
Brantly Callaway 2018-04-30
The R qte package implements many methods used, especially in economics, to estimate quantile treatment effects. These include the case where treatment is randomly assigned, under selection on observables, under a Difference in Differences Assumtpion.
The package is available on CRAN and can be loaded as follows
library(qte)
The following example shows how to use the ci.qte method in the qte package using data about an experimental job training program.
data(lalonde)
jt.cia <- ci.qte(re78 ~ treat,
xformla=~age + education + black + hispanic + married + nodegree,
data=lalonde.psid,
probs=seq(0.05,0.95,0.05), se=T)
summary(jt.cia)
More examples and details about other functions in the package can be found at the package's website.