calls function to compute counterfactuals
cfa.inner(tvals, yvals, data, yname, tname, xnames = NULL,
method = "dr", link = "logit", tau = seq(0.01, 0.99, 0.01),
condDistobj = NULL, se = TRUE, iters = 100, cl = 1)the values of the "treatment" to compute parameters of interest for
the values to compute the counterfactual distribution for
the data.frame where y, t, and x are
the name of the outcome (y) variable
the name of the treatment (t) variable
the names of additional control variables to include
either "dr" or "qr" for distribution regression or quantile regression
if using distribution regression, any link function that works with the binomial family (e.g. logit (the default), probit, cloglog)
if using quantile regression, which values of tau to estimate the conditional quantiles
optional conditional distribution object that has been previously computed
whether or not to compute standard errors using the bootstrap
how many bootstrap iterations to use
how many clusters to use for parallel computation of standard errors
CFA object