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 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, nonparametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using the conditional density estimation procedure to estimate the generalized propensity score. Per Hejazi, Benkeser, Díaz, and van der Laan <>10.48550/arXiv.2205.05777>, these nonparametric IPW estimators can be coupled with sieve estimation (undersmoothing) of the generalized propensity score estimators to attain the non/semi-parametric efficiency bound.
|Maintainer||Nima Hejazi <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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