doi:10.2202/1557-4679.1356>. Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan <doi:10.48550/arXiv.2205.05777>).">

haldensify: Highly Adaptive Lasso Conditional Density Estimation (original) (raw)

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 flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan <doi:10.48550/arXiv.2205.05777>).

Version: 0.2.8
Depends: R (≥ 3.2.0)
Imports: stats, utils, dplyr, tibble, ggplot2, data.table, matrixStats, future.apply, assertthat, hal9001 (≥ 0.4.6), origami (≥ 1.0.7), stringr, rlang, scales, Rdpack
Suggests: testthat, knitr, rmarkdown, covr, future
Published: 2025-09-02
DOI: 10.32614/CRAN.package.haldensify
Author: Nima Hejazi ORCID iD [aut, cre, cph], David Benkeser ORCID iD [aut], Mark van der Laan ORCID iD [aut, ths], Rachael Phillips ORCID iD [ctb]
Maintainer: Nima Hejazi
BugReports: https://github.com/nhejazi/haldensify/issues
License: MIT + file
URL: https://codex.nimahejazi.org/haldensify/
NeedsCompilation: no
Citation: haldensify citation info
Materials: README, NEWS
CRAN checks: haldensify results

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