compute.cfa2

the values of the "treatment" to compute parameters of
interest for

tvals

the values to compute the counterfactual distribution for

yvals

the data.frame where y, t, and x are

data

either "dr" or "qr" for distribution regression or quantile regression

method

if using distribution regression, any link function that works with the binomial family (e.g. logit (the default), probit, cloglog)

link

if using quantile regression, which values of tau to estimate
the conditional quantiles

optional conditional distribution object that has
been previously computed

condDistobj

optional pre-estimated conditional distribution function

condDist

an update of compute.cfa that allows one to use other
 other estimators such as quantile regression or nonparametric methods
 (not yet implemented) as the first step estimator of the conditional
 distribution

internal

Contains methods for computing counterfactuals with a continuous treatment variable as in Callaway and Huang (2017) <https://ssrn.com/abstract=3078187>. In particular, the package can be used to calculate the expected value, the variance, the interquantile range, the fraction of observations below or above a particular cutoff, or other user-supplied functions of an outcome of interest conditional on a continuous treatment. The package can also be used for computing these same functionals after adjusting for differences in covariates at different values of the treatment. Further, one can use the package to conduct uniform inference for each parameter of interest across all values of the treatment, uniformly test whether adjusting for covariates makes a difference at any value of the treatment, and test whether a parameter of interest is different from its average value at an value of the treatment.

compute.cfa2: compute.cfa2

Description

Usage

Arguments

Value