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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<style type="text/css">
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color: #777;
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line-height: 1.1;
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margin-bottom: 16px;
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margin: 16px 0;
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padding-left: 2em;
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ul ol,
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font-weight: bold;
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margin-bottom: 16px;
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blockquote {
padding: 0 15px;
color: #777;
border-left: 4px solid #ddd;
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blockquote>:last-child {
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width: 100%;
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word-break: keep-all;
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font-weight: bold;
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padding: 6px 13px;
border: 1px solid #ddd;
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background-color: #fff;
border-top: 1px solid #ccc;
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background-color: #f8f8f8;
}
img {
max-width: 100%;
box-sizing: content-box;
background-color: #fff;
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code {
padding: 0;
padding-top: 0.2em;
padding-bottom: 0.2em;
margin: 0;
font-size: 85%;
background-color: rgba(0,0,0,0.04);
border-radius: 3px;
}
code:before,
code:after {
letter-spacing: -0.2em;
content: "\00a0";
}
pre>code {
padding: 0;
margin: 0;
font-size: 100%;
word-break: normal;
white-space: pre;
background: transparent;
border: 0;
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margin-bottom: 16px;
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pre {
padding: 16px;
overflow: auto;
font-size: 85%;
line-height: 1.45;
background-color: #f7f7f7;
border-radius: 3px;
}
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margin-bottom: 0;
word-break: normal;
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pre {
word-wrap: normal;
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pre code {
display: inline;
max-width: initial;
padding: 0;
margin: 0;
overflow: initial;
line-height: inherit;
word-wrap: normal;
background-color: transparent;
border: 0;
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pre code:after {
content: normal;
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padding: 3px 5px;
font-size: 11px;
line-height: 10px;
color: #555;
vertical-align: middle;
background-color: #fcfcfc;
border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
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color: #969896;
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color: #ed6a43;
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font-weight: bold;
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font-weight: bold;
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padding: 3px 5px;
font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;
line-height: 10px;
color: #555;
vertical-align: middle;
background-color: #fcfcfc;
border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
}
.task-list-item {
list-style-type: none;
}
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z-index: 1;
position: relative;
border-color: #4078c0;
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code .kw { color: #000000; }
code .dt { color: #ed6a43; }
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code .bn { color: #009999; }
code .fl { color: #009999; }
code .ch { color: #009999; }
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code .co { color: #969896; }
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code .cv { color: #008080; }
code .in { color: #008080; }
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body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
padding-top: 0px;
}
</style>
</head>
<body>
<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="rhaldensify">R/<code>haldensify</code></h1>
<!-- badges: start -->
<p><a href="https://github.com/nhejazi/haldensify/actions"><img src="data:image/svg+xml; charset=utf-8;base64,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" alt="R-CMD-check" /></a> <a href="https://codecov.io/github/nhejazi/haldensify?branch=master"><img src="data:image/svg+xml;charset=utf-8;base64,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" alt="Coverage Status" /></a> <a href="https://www.r-pkg.org/pkg/haldensify"><img src="data:image/svg+xml; 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<!-- badges: end -->
<blockquote>
<p>Highly Adaptive Lasso Conditional Density Estimation</p>
</blockquote>
<p><strong>Authors:</strong> <a href="https://nimahejazi.org">Nima
Hejazi</a>, <a href="https://www.sph.emory.edu/faculty/profile/#!dbenkes">David
Benkeser</a>, and <a href="https://vanderlaan-lab.org/about/">Mark van
der Laan</a></p>
<hr />
<h2 id="whats-haldensify">What’s <code>haldensify</code>?</h2>
<p>The <code>haldensify</code> R package is designed to provide
facilities for nonparametric conditional density estimation based on a
flexible procedure proposed initially by Dı́az and van der Laan (2011).
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 <em>generalized propensity score</em>
(for continuous-valued exposures) must be estimated (Dı́az and van der
Laan 2012, 2018; Dı́az and Hejazi 2020). <code>haldensify</code>
implements this conditional density estimation strategy for use only
with the highly adaptive lasso (HAL) (Benkeser and van der Laan 2016;
van der Laan 2017; van der Laan and Benkeser 2018; Coyle et al. 2022;
Hejazi, Coyle, and van der Laan 2020). Since the generalized propensity
score is a key ingredient in inverse probability weighting (IPW)
methods, <code>haldensify</code> builds on the advances of Ertefaie,
Hejazi, and van der Laan (2022) and Hejazi et al. (2022) 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.</p>
<hr />
<h2 id="installation">Installation</h2>
<p>For standard use, we recommend installing the package from <a href="https://CRAN.R-project.org/package=haldensify">CRAN</a> via</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"haldensify"</span>)</span></code></pre></div>
<p>To contribute, install the <em>development version</em> of
<code>haldensify</code> from GitHub via <a href="https://CRAN.R-project.org/package=remotes"><code>remotes</code></a>:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"nhejazi/haldensify"</span>)</span></code></pre></div>
<hr />
<h2 id="example">Example</h2>
<p>A simple example illustrates how <code>haldensify</code> may be used
to train a highly adaptive lasso model to obtain conditional density
estimates:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(haldensify)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co">#> haldensify v0.2.5: Highly Adaptive Lasso Conditional Density Estimation</span></span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">76924</span>)</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># simulate data: W ~ U[-4, 4] and A|W ~ N(mu = W, sd = 0.25)</span></span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a>n_train <span class="ot"><-</span> <span class="dv">100</span></span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a>w <span class="ot"><-</span> <span class="fu">runif</span>(n_train, <span class="sc">-</span><span class="dv">4</span>, <span class="dv">4</span>)</span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a>a <span class="ot"><-</span> <span class="fu">rnorm</span>(n_train, w, <span class="fl">0.25</span>)</span>
<span id="cb3-9"><a href="#cb3-9" tabindex="-1"></a></span>
<span id="cb3-10"><a href="#cb3-10" tabindex="-1"></a><span class="co"># HAL-based density estimate of A|W</span></span>
<span id="cb3-11"><a href="#cb3-11" tabindex="-1"></a>haldensify_fit <span class="ot"><-</span> <span class="fu">haldensify</span>(</span>
<span id="cb3-12"><a href="#cb3-12" tabindex="-1"></a> <span class="at">A =</span> a, <span class="at">W =</span> w,</span>
<span id="cb3-13"><a href="#cb3-13" tabindex="-1"></a> <span class="at">n_bins =</span> <span class="dv">10</span>, <span class="at">grid_type =</span> <span class="st">"equal_range"</span>,</span>
<span id="cb3-14"><a href="#cb3-14" tabindex="-1"></a> <span class="at">lambda_seq =</span> <span class="fu">exp</span>(<span class="fu">seq</span>(<span class="sc">-</span><span class="dv">1</span>, <span class="sc">-</span><span class="dv">10</span>, <span class="at">length =</span> <span class="dv">100</span>)),</span>
<span id="cb3-15"><a href="#cb3-15" tabindex="-1"></a> <span class="co"># arguments passed to hal9001::fit_hal()</span></span>
<span id="cb3-16"><a href="#cb3-16" tabindex="-1"></a> <span class="at">max_degree =</span> <span class="dv">3</span>,</span>
<span id="cb3-17"><a href="#cb3-17" tabindex="-1"></a> <span class="at">reduce_basis =</span> <span class="dv">1</span> <span class="sc">/</span> <span class="fu">sqrt</span>(n_train)</span>
<span id="cb3-18"><a href="#cb3-18" tabindex="-1"></a>)</span>
<span id="cb3-19"><a href="#cb3-19" tabindex="-1"></a><span class="co">#> Warning in (function (X, Y, formula = NULL, X_unpenalized = NULL, max_degree = ifelse(ncol(X) >= : Some fit_control arguments are neither default nor glmnet/cv.glmnet arguments: n_folds; </span></span>
<span id="cb3-20"><a href="#cb3-20" tabindex="-1"></a><span class="co">#> They will be removed from fit_control</span></span>
<span id="cb3-21"><a href="#cb3-21" tabindex="-1"></a>haldensify_fit</span>
<span id="cb3-22"><a href="#cb3-22" tabindex="-1"></a><span class="co">#> HAL Conditional Density Estimation</span></span>
<span id="cb3-23"><a href="#cb3-23" tabindex="-1"></a><span class="co">#> Number of bins over support of A: 10</span></span>
<span id="cb3-24"><a href="#cb3-24" tabindex="-1"></a><span class="co">#> CV-selected lambda: 0.0016</span></span>
<span id="cb3-25"><a href="#cb3-25" tabindex="-1"></a><span class="co">#> Summary of fitted HAL:</span></span>
<span id="cb3-26"><a href="#cb3-26" tabindex="-1"></a><span class="co">#> coef term</span></span>
<span id="cb3-27"><a href="#cb3-27" tabindex="-1"></a><span class="co">#> 1: 5.989688 (Intercept)</span></span>
<span id="cb3-28"><a href="#cb3-28" tabindex="-1"></a><span class="co">#> 2: 10.498800 [ I(bin_id >= 2) ]</span></span>
<span id="cb3-29"><a href="#cb3-29" tabindex="-1"></a><span class="co">#> 3: -9.673620 [ I(W >= -3.353) ]</span></span>
<span id="cb3-30"><a href="#cb3-30" tabindex="-1"></a><span class="co">#> 4: 8.659440 [ I(bin_id >= 6) ]</span></span>
<span id="cb3-31"><a href="#cb3-31" tabindex="-1"></a><span class="co">#> 5: -8.272041 [ I(bin_id >= 2) ] * [ I(W >= -2.371) ]</span></span>
<span id="cb3-32"><a href="#cb3-32" tabindex="-1"></a><span class="co">#> 6: -8.261273 [ I(W >= -3.109) ]</span></span>
<span id="cb3-33"><a href="#cb3-33" tabindex="-1"></a><span class="co">#> 7: 8.054827 [ I(bin_id >= 7) ]</span></span>
<span id="cb3-34"><a href="#cb3-34" tabindex="-1"></a><span class="co">#> 8: 8.013383 [ I(bin_id >= 4) ]</span></span>
<span id="cb3-35"><a href="#cb3-35" tabindex="-1"></a><span class="co">#> 9: 8.001995 [ I(bin_id >= 5) ]</span></span>
<span id="cb3-36"><a href="#cb3-36" tabindex="-1"></a><span class="co">#> 10: -7.649731 [ I(W >= -2.157) ]</span></span></code></pre></div>
<p>We can also visualize the empirical risk (with respect to density
loss) in terms of the solution path of the lasso regularization
parameter:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># just use the built-in plot method</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">plot</span>(haldensify_fit)</span></code></pre></div>
<img src="data:image/png;base64,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" width="80%" />
<p>Finally, we can obtain conditional density estimates from the trained
model on the training (or on new) data:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co"># use the built-in predict method to get predictions</span></span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>pred_haldensify <span class="ot"><-</span> <span class="fu">predict</span>(haldensify_fit, <span class="at">new_A =</span> a, <span class="at">new_W =</span> w)</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">head</span>(pred_haldensify)</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co">#> [1] 0.2818730 0.5513780 0.4449961 0.5329549 0.8722028 0.6150810</span></span></code></pre></div>
<p>For more details, check out the <a href="https://code.nimahejazi.org/haldensify/articles/intro_haldensify">package
vignette</a> on the corresponding <code>pkgdown</code> site.</p>
<hr />
<h2 id="issues">Issues</h2>
<p>If you encounter any bugs or have any specific feature requests,
please <a href="https://github.com/nhejazi/haldensify/issues">file an
issue</a>.</p>
<hr />
<h2 id="contributions">Contributions</h2>
<p>Contributions are very welcome. Interested contributors should
consult our <a href="https://github.com/nhejazi/haldensify/blob/master/CONTRIBUTING.md">contribution
guidelines</a> prior to submitting a pull request.</p>
<hr />
<h2 id="citation">Citation</h2>
<p>After using the <code>haldensify</code> R package, please cite the
following:</p>
<pre><code> @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}
}</code></pre>
<hr />
<h2 id="related">Related</h2>
<ul>
<li><a href="https://github.com/tlverse/hal9001">R/<code>hal9001</code></a> –
The highly adaptive lasso estimator used internally to constructed
conditional density estimates.</li>
</ul>
<hr />
<h2 id="funding">Funding</h2>
<p>The development of this software was supported in part through grants
from the National Library of Medicine (award number <a href="https://reporter.nih.gov/project-details/9248418">T32
LM012417</a>), the National Institute of Allergy and Infectious Diseases
(award number <a href="https://reporter.nih.gov/project-details/9926564">R01
AI074345</a>) of the National Institutes of Health, and the National
Science Foundation (award number <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102840">DMS
2102840</a>).</p>
<hr />
<h2 id="license">License</h2>
<p>© 2019-2024 <a href="https://nimahejazi.org">Nima S. Hejazi</a></p>
<p>The contents of this repository are distributed under the MIT
license. See below for details:</p>
<pre><code>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.</code></pre>
<hr />
<h2 id="references">References</h2>
<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
<div id="ref-benkeser2016highly" class="csl-entry">
<p>Benkeser, David, and Mark J van der Laan. 2016. “The Highly Adaptive
Lasso Estimator.” In <em>Proceedings of the International Conference on
Data Science and Advanced Analytics</em>, 2016:689. NIH Public Access.
<a href="https://doi.org/10.1109/dsaa.2016.93">https://doi.org/10.1109/dsaa.2016.93</a>.</p>
</div>
<div id="ref-coyle2022hal9001-rpkg" class="csl-entry">
<p>Coyle, Jeremy R, Nima S Hejazi, Rachael V Phillips, Lars WP van der
Laan, and Mark J van der Laan. 2022.
<em><span class="nocase">hal9001</span>: The Scalable Highly Adaptive
Lasso</em>. <a href="https://doi.org/10.5281/zenodo.3558313">https://doi.org/10.5281/zenodo.3558313</a>.</p>
</div>
<div id="ref-diaz2020causal" class="csl-entry">
<p>Dı́az, Iván, and Nima S Hejazi. 2020. “Causal Mediation Analysis for
Stochastic Interventions.” <em>Journal of the Royal Statistical Society:
Series B (Statistical Methodology)</em> 82 (3): 661–83. <a href="https://doi.org/10.1111/rssb.12362">https://doi.org/10.1111/rssb.12362</a>.</p>
</div>
<div id="ref-diaz2011super" class="csl-entry">
<p>Dı́az, Iván, and Mark J van der Laan. 2011. “Super Learner Based
Conditional Density Estimation with Application to Marginal Structural
Models.” <em>International Journal of Biostatistics</em> 7 (1): 1–20. <a href="https://doi.org/10.2202/1557-4679.1356">https://doi.org/10.2202/1557-4679.1356</a>.</p>
</div>
<div id="ref-diaz2012population" class="csl-entry">
<p>———. 2012. “Population Intervention Causal Effects Based on
Stochastic Interventions.” <em>Biometrics</em> 68 (2): 541–49. <a href="https://doi.org/10.1111/j.1541-0420.2011.01685.x">https://doi.org/10.1111/j.1541-0420.2011.01685.x</a>.</p>
</div>
<div id="ref-diaz2018stochastic" class="csl-entry">
<p>———. 2018. “Stochastic Treatment Regimes.” In <em>Targeted Learning
in Data Science: Causal Inference for Complex Longitudinal Studies</em>,
167–80. Springer. <a href="https://doi.org/10.1007/978-3-319-65304-4_14">https://doi.org/10.1007/978-3-319-65304-4_14</a>.</p>
</div>
<div id="ref-ertefaie2020nonparametric" class="csl-entry">
<p>Ertefaie, Ashkan, Nima S Hejazi, and Mark J van der Laan. 2022.
“Nonparametric Inverse-Probability-Weighted Estimators Based on the
Highly Adaptive Lasso.” <em>Biometrics</em> (in press). <a href="https://doi.org/10.1111/biom.13719">https://doi.org/10.1111/biom.13719</a>.</p>
</div>
<div id="ref-hejazi2022efficient" class="csl-entry">
<p>Hejazi, Nima S, David Benkeser, Iván Dı́az, and Mark J van der Laan.
2022. “Efficient Estimation of Modified Treatment Policy Effects Based
on the Generalized Propensity Score.” <em>arXiv</em>. <a href="https://arxiv.org/abs/2205.05777">https://arxiv.org/abs/2205.05777</a>.</p>
</div>
<div id="ref-hejazi2020hal9001-joss" class="csl-entry">
<p>Hejazi, Nima S, Jeremy R Coyle, and Mark J van der Laan. 2020.
“<span class="nocase">hal9001</span>: Scalable Highly Adaptive Lasso
Regression in R.” <em>Journal of Open Source Software</em>. <a href="https://doi.org/10.21105/joss.02526">https://doi.org/10.21105/joss.02526</a>.</p>
</div>
<div id="ref-vdl2017generally" class="csl-entry">
<p>van der Laan, Mark J. 2017. “A Generally Efficient Targeted Minimum
Loss-Based Estimator Based on the Highly Adaptive Lasso.”
<em>International Journal of Biostatistics</em> 13 (2). <a href="https://doi.org/10.1515/ijb-2015-0097">https://doi.org/10.1515/ijb-2015-0097</a>.</p>
</div>
<div id="ref-vdl2018highly" class="csl-entry">
<p>van der Laan, Mark J, and David Benkeser. 2018. “Highly Adaptive
Lasso (HAL).” In <em>Targeted Learning in Data Science: Causal Inference
for Complex Longitudinal Studies</em>, 77–94. Springer. <a href="https://doi.org/10.1007/978-3-319-65304-4_6">https://doi.org/10.1007/978-3-319-65304-4_6</a>.</p>
</div>
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