cia.bounds: cia.bounds

In bcallaway11/csabounds: Bounds on Distributional Treatment Effect Parameters

cia.bounds

Description

Bounds on the distribution of the treatment effect and on the quantile of the treatment effect under a conditional independence assumption (cia). It takes in a data.frame, that should indicate whether or not an individual is treated; separates individuals into a treated and untreated group; runs distribution or quantile regression to estimate the conditional distributions; then computes bounds on the DoTT or QoTT.

Usage

cia.bounds(
  formla,
  xformla = ~1,
  data,
  delt.seq,
  y.seq = NULL,
  firststep = c("dr", "qr", "ll"),
  link = "logit",
  se = FALSE,
  bootiters = 100,
  cl = 1,
  alp = 0.05,
  ...
)

Arguments

formla	y ~ d
xformla	~ x1 + x2
data	the name of the data.frame. It should be in "long" format rather than "wide" format.
delt.seq	a vector of values to compute the distribution of the treatment effect for
y.seq	a vectof of values to compute first-step distributions over (this is currently not used as it is computed internally)
firststep	whether to use distribution regression ("dr"), quantile regression ("qr"), or local linear distribution regression ("ll") for the first step estimation of condtional distributions
link	optional argument to pass to distreg for which link function to use when running distribution regressions
se	whether or not to compute standard errors (if TRUE, they are computed using the bootstrap
bootiters	if computing standard errors using the bootstrap, how many bootstrap iterations to use
cl	if computing standard errors using the bootstrap, how many cores to use in parallel computation (default is 1)
alp	significance level for confidence intervals
...	whatever extra arguments need to be passed to Y0tmethod

bcallaway11/csabounds documentation built on July 27, 2022, 5:17 p.m.