Description Usage Arguments Value References Examples
cv.cdensity
estimates counterfactual densities using
linear cosine basis expansions at a sequence of dimensions, and then estimates
the L2 pseudorisk of each, which can be used for purposes of model selection.
Nuisance functions are estimated with random forests.
1 2  cv.cdensity(y, a, x, kmax=5,
gridlen=20, nsplits=2, progress_updates = TRUE)

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. 
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 
A plot of the pseudo L2 risk of candidate estimators for counterfactual densities, at each model dimension from 1 to kmax
Kennedy EH, Wasserman LA, Balakrishnan S. Semiparametric counterfactual density estimation. arxiv:TBA
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