knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = "100%", fig.path = "README-" )
haldensify
Highly Adaptive Lasso Conditional Density Estimation
Authors: Nima Hejazi, David Benkeser, and Mark van der Laan
haldensify
?The haldensify
R package is designed to provide facilities for nonparametric
conditional density estimation based on a flexible procedure proposed initially
by @diaz2011super. The core of the implemented methodology involves recovering
conditional density estimates by performing pooled hazards regressions so as to
assess the conditional hazard that an observed value falls in a given bin over
the (conditional) support of the variable of interest. Such conditional density
estimates are useful, for example, in causal inference problems in which the
generalized propensity score (for continuous-valued exposures) must be
estimated [@diaz2012population; @diaz2018stochastic; @diaz2020causal].
haldensify
implements this conditional density estimation strategy for use
only with the highly adaptive lasso (HAL) [@benkeser2016highly;
@vdl2017generally; @vdl2018highly; @coyle2022hal9001-rpkg;
@hejazi2020hal9001-joss]. Since the generalized propensity score is a key
ingredient in inverse probability weighting (IPW) methods, haldensify
builds
on the advances of @ertefaie2020nonparametric and @hejazi2022efficient to
provide nonparametric IPW estimators of the causal effects for continuous
treatments, which achieve the semiparametric efficiency bound by undersmoothing
along a family of HAL conditional density estimators.
For standard use, we recommend installing the package from CRAN via
install.packages("haldensify")
To contribute, install the development version of haldensify
from GitHub
via remotes
:
remotes::install_github("nhejazi/haldensify")
A simple example illustrates how haldensify
may be used to train a highly
adaptive lasso model to obtain conditional density estimates:
library(haldensify) set.seed(76924) # simulate data: W ~ U[-4, 4] and A|W ~ N(mu = W, sd = 0.25) n_train <- 100 w <- runif(n_train, -4, 4) a <- rnorm(n_train, w, 0.25) # HAL-based density estimate of A|W haldensify_fit <- haldensify( A = a, W = w, n_bins = 10, grid_type = "equal_range", lambda_seq = exp(seq(-1, -10, length = 100)), # arguments passed to hal9001::fit_hal() max_degree = 3, reduce_basis = 1 / sqrt(n_train) ) haldensify_fit
We can also visualize the empirical risk (with respect to density loss) in terms of the solution path of the lasso regularization parameter:
# just use the built-in plot method plot(haldensify_fit)
Finally, we can obtain conditional density estimates from the trained model on the training (or on new) data:
# use the built-in predict method to get predictions pred_haldensify <- predict(haldensify_fit, new_A = a, new_W = w) head(pred_haldensify)
For more details, check out the package
vignette on
the corresponding pkgdown
site.
If you encounter any bugs or have any specific feature requests, please file an issue.
Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
After using the haldensify
R package, please cite the following:
@article{hejazi2022efficient, author = {Hejazi, Nima S and Benkeser, David and D{\'\i}az, Iv{\'a}n and {van der Laan}, Mark J}, title = {Efficient estimation of modified treatment policy effects based on the generalized propensity score}, year = {2022}, journal = {}, publisher = {}, volume = {}, number = {}, pages = {}, doi = {}, url = {https://arxiv.org/abs/2205.05777} } @article{hejazi2022haldensify-joss, author = {Hejazi, Nima S and {van der Laan}, Mark J and Benkeser, David C}, title = {{haldensify}: Highly adaptive lasso conditional density estimation in {R}}, year = {2022}, doi = {10.21105/joss.04522}, url = {https://doi.org/10.21105/joss.04522}, journal = {Journal of Open Source Software}, publisher = {The Open Journal} } @software{hejazi2022haldensify-rpkg, author = {Hejazi, Nima S and Benkeser, David C and {van der Laan}, Mark J}, title = {{haldensify}: Highly adaptive lasso conditional density estimation}, year = {2022}, howpublished = {\url{https://github.com/nhejazi/haldensify}}, doi = {10.5281/zenodo.3698329}, url = {https://doi.org/10.5281/zenodo.3698329}, note = {{R} package version 0.2.5} }
hal9001
-- The highly adaptive
lasso estimator used internally to constructed conditional density estimates.The development of this software was supported in part through grants from the National Library of Medicine (award number T32 LM012417), the National Institute of Allergy and Infectious Diseases (award number R01 AI074345) of the National Institutes of Health, and the National Science Foundation (award number DMS 2102840).
© 2019-2024 Nima S. Hejazi
The contents of this repository are distributed under the MIT license. See below for details:
MIT License Copyright (c) 2019-2024 Nima S. Hejazi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.