ci.qtet — ci.qtet • qte

Deprecated. Use unc_qte(target = "qtt") for a cross-sectional QTT under unconfoundedness, or lou_qtt for staggered treatment adoption with optional lagged-outcome conditioning.

ci.qtet estimates the Quantile Treatment Effect on the Treated (QTET) under a Conditional Independence Assumption (sometimes called Selection on Observables) following Firpo (2007). It uses propensity score re-weighting to estimate the counterfactual distribution. Standard errors are computed via the bootstrap.

Usage

ci.qtet(
  formla,
  xformla = NULL,
  w = NULL,
  data,
  probs = seq(0.05, 0.95, 0.05),
  se = TRUE,
  iters = 100,
  alp = 0.05,
  method = "logit",
  retEachIter = FALSE,
  indsample = TRUE,
  printIter = FALSE,
  pl = FALSE,
  cores = 2,
  biters = NULL,
  cl = NULL
)

Arguments

formla: The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
xformla: A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
w: sampling weight vector. Cannot be forwarded automatically; use weightsname in unc_qte instead.
data: A data.frame containing all the variables used
probs: A vector of values between 0 and 1 to compute the QTET at
se: Boolean whether or not to compute standard errors
iters: The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
alp: The significance level used for constructing bootstrap confidence intervals
method: Method to compute propensity score. Default is logit; other option is probit.
retEachIter: Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
indsample: Binary variable for whether to treat the samples as independent or dependent. This affects bootstrap standard errors. In the job training example, the samples are independent because they are two samples collected independently and then merged. If the data is from the same source, usually should set this option to be FALSE.
printIter: For debugging only; should leave at default FALSE unless you want to see a lot of output
pl: Whether or not to compute standard errors in parallel
cores: Number of cores to use if computing in parallel
biters: Number of bootstrap iterations; alias for iters matching the did/ptetools naming convention. If both are supplied, biters takes precedence.
cl: Number of cores for parallel bootstrap; alias for pl/cores. cl = 1 (default) runs sequentially; cl > 1 enables parallelism.

Value

A QTE object; same structure as unc_qte.

References

Firpo, Sergio. “Efficient Semiparametric Estimation of Quantile Treatment Effects.” Econometrica 75.1, pp. 259-276, 2007.

Examples

# See ?unc_qte for the modern replacement.
if (FALSE) { # \dontrun{
data(lalonde)

## Estimate the QTET of participating in the job training program;
## This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qtet(re78 ~ treat,
  x = NULL, data = lalonde.psid, se = FALSE,
  probs = seq(0.05, 0.95, 0.05)
)
summary(q1)

## This estimation controls for all the available background characteristics.
q2 <- ci.qtet(re78 ~ treat,
  xformla = ~ age + I(age^2) + education + black + hispanic + married + nodegree,
  data = lalonde.psid, se = FALSE, probs = seq(0.05, 0.95, 0.05)
)
summary(q2)
} # }