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 density-based 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 plug-in 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/p-values for population means and relevant contrasts. |
nuis |
subject-specific 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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