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 pseudo-risk 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 plug-in 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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