Description Usage Arguments Value References Examples
cdensity
is used to estimate counterfactual densities,
i.e., the density of the potential outcome in a population if everyone
received given treatment levels, using doubly robust estimates of L2
projections of the density onto a linear basis expansion. Nuisance functions
are estimated with random forests. The L2 distance between the density of the
counterfactuals is also estimated as a densitybased treatment effect.
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y 
outcome of interest. 
a 
binary treatment (more than 2 levels are allowed, but only densities under A=1 and A=0 will be estimated). 
x 
covariate matrix. 
kmax 
Integer indicating maximum dimension of (cosine) basis expansion that should be used in series estimator. 
l2 
A 
gridlen 
Integer number indicating length of grid for which the plugin estimator of the marginal density is computed. 
nsplits 
Integer number of sample splits for nuisance estimation. If

progress_updates 
A 
makeplot 
A 
kforplot 
A vector of two integers indicating which k values to plot results for, with first argument for A=1 and second for A=0. 
ylim 
Range of y values at which density should be plotted. 
A list containing the following components:
res 
estimates/SEs/CIs/pvalues for population means and relevant contrasts. 
nuis 
subjectspecific estimates of nuisance functions (i.e., propensity score and outcome regression) 
ifvals 
matrix of estimated influence function values. 
Kennedy EH, Wasserman LA, Balakrishnan S. Semiparametric counterfactual density estimation. arxiv:TBA
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