An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate non/semi-parametric estimation of the conditional density, the highly adaptive lasso, a nonparametric regression function shown to reliably estimate a large class of functions at a fast convergence rate, is utilized. The pooled hazards data augmentation formulation implemented was first described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To complement the conditional density estimation utilities, tools for efficient nonparametric inverse probability weighted (IPW) estimation of the causal effects of stochastic shift interventions (modified treatment policies), directly utilizing the density estimation technique for construction of the generalized propensity score, are provided. These IPW estimators utilize undersmoothing (sieve estimation) of the conditional density estimators in order to achieve the non/semi-parametric efficiency bound.
|Author||Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>), Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>), Rachael Phillips [ctb] (<https://orcid.org/0000-0002-8474-591X>)|
|Maintainer||Nima Hejazi <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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