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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" />
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<h1 id="rmedshift">R/<code>medshift</code></h1>
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<p><a href="https://github.com/nhejazi/medshift/actions"><img src="data:image/svg+xml; 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<blockquote>
<p>Causal Mediation Analysis for Stochastic Interventions</p>
</blockquote>
<p><strong>Authors:</strong> <a href="https://nimahejazi.org">Nima Hejazi</a> and <a href="https://idiaz.xyz">Iván Díaz</a></p>
<hr />
<h2 id="whats-medshift">What’s <code>medshift</code>?</h2>
<p>The <code>medshift</code> R package is designed to provide facilities for estimating a parameter that arises in a decomposition of the population intervention causal effect into the (in)direct effects under stochastic interventions in the setting of mediation analysis. <code>medshift</code> is designed as an implementation to accompany the methodology described in Dı́az and Hejazi (2020). Implemented estimators include the classical substitution (G-computation) estimator, an inverse probability weighted (IPW) estimator, an efficient one-step estimator using cross-fitting (Pfanzagl and Wefelmeyer 1985; Zheng and van der Laan 2011; Chernozhukov et al. 2018), and a cross-validated targeted minimum loss (TML) estimator (van der Laan and Rose 2011; Zheng and van der Laan 2011). <code>medshift</code> integrates with the <a href="https://github.com/tlverse/sl3"><code>sl3</code> R package</a> (Coyle et al. 2022) to allow constructed estimators to leverage machine learning for nuisance estimation.</p>
<hr />
<h2 id="installation">Installation</h2>
<p>Install the <em>most recent version</em> from the <code>master</code> branch on GitHub via <a href="https://CRAN.R-project.org/package=remotes"><code>remotes</code></a>:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">remotes<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"nhejazi/medshift"</span>)</a></code></pre></div>
<hr />
<h2 id="example">Example</h2>
<p>To illustrate how <code>medshift</code> may be used to estimate the effect of applying a stochastic intervention to the treatment (<code>A</code>) while keeping the mediator(s) (<code>Z</code>) fixed, consider the following example:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw">library</span>(data.table)</a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw">library</span>(medshift)</a>
<a class="sourceLine" id="cb2-3" title="3"></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="co"># produces a simple data set based on ca causal model with mediation</span></a>
<a class="sourceLine" id="cb2-5" title="5">make_simple_mediation_data <-<span class="st"> </span><span class="cf">function</span>(<span class="dt">n_obs =</span> <span class="dv">1000</span>) {</a>
<a class="sourceLine" id="cb2-6" title="6"> <span class="co"># baseline covariate -- simple, binary</span></a>
<a class="sourceLine" id="cb2-7" title="7"> W <-<span class="st"> </span><span class="kw">rbinom</span>(n_obs, <span class="dv">1</span>, <span class="dt">prob =</span> <span class="fl">0.50</span>)</a>
<a class="sourceLine" id="cb2-8" title="8"></a>
<a class="sourceLine" id="cb2-9" title="9"> <span class="co"># create treatment based on baseline W</span></a>
<a class="sourceLine" id="cb2-10" title="10"> A <-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">rbinom</span>(n_obs, <span class="dv">1</span>, <span class="dt">prob =</span> W <span class="op">/</span><span class="st"> </span><span class="dv">4</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.1</span>))</a>
<a class="sourceLine" id="cb2-11" title="11"></a>
<a class="sourceLine" id="cb2-12" title="12"> <span class="co"># single mediator to affect the outcome</span></a>
<a class="sourceLine" id="cb2-13" title="13"> z1_prob <-<span class="st"> </span><span class="dv">1</span> <span class="op">-</span><span class="st"> </span><span class="kw">plogis</span>((A<span class="op">^</span><span class="dv">2</span> <span class="op">+</span><span class="st"> </span>W) <span class="op">/</span><span class="st"> </span>(A <span class="op">+</span><span class="st"> </span>W<span class="op">^</span><span class="dv">3</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.5</span>))</a>
<a class="sourceLine" id="cb2-14" title="14"> Z <-<span class="st"> </span><span class="kw">rbinom</span>(n_obs, <span class="dv">1</span>, <span class="dt">prob =</span> z1_prob)</a>
<a class="sourceLine" id="cb2-15" title="15"></a>
<a class="sourceLine" id="cb2-16" title="16"> <span class="co"># create outcome as a linear function of A, W + white noise</span></a>
<a class="sourceLine" id="cb2-17" title="17"> Y <-<span class="st"> </span>Z <span class="op">+</span><span class="st"> </span>A <span class="op">-</span><span class="st"> </span><span class="fl">0.1</span> <span class="op">*</span><span class="st"> </span>W <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(n_obs, <span class="dt">mean =</span> <span class="dv">0</span>, <span class="dt">sd =</span> <span class="fl">0.25</span>)</a>
<a class="sourceLine" id="cb2-18" title="18"></a>
<a class="sourceLine" id="cb2-19" title="19"> <span class="co"># full data structure</span></a>
<a class="sourceLine" id="cb2-20" title="20"> data <-<span class="st"> </span><span class="kw">as.data.table</span>(<span class="kw">cbind</span>(Y, Z, A, W))</a>
<a class="sourceLine" id="cb2-21" title="21"> <span class="kw">setnames</span>(data, <span class="kw">c</span>(<span class="st">"Y"</span>, <span class="st">"Z"</span>, <span class="st">"A"</span>, <span class="st">"W"</span>))</a>
<a class="sourceLine" id="cb2-22" title="22"> <span class="kw">return</span>(data)</a>
<a class="sourceLine" id="cb2-23" title="23">}</a>
<a class="sourceLine" id="cb2-24" title="24"></a>
<a class="sourceLine" id="cb2-25" title="25"><span class="co"># set seed and simulate example data</span></a>
<a class="sourceLine" id="cb2-26" title="26"><span class="kw">set.seed</span>(<span class="dv">75681</span>)</a>
<a class="sourceLine" id="cb2-27" title="27">example_data <-<span class="st"> </span><span class="kw">make_simple_mediation_data</span>()</a>
<a class="sourceLine" id="cb2-28" title="28"></a>
<a class="sourceLine" id="cb2-29" title="29"><span class="co"># compute one-step estimate for an incremental propensity score intervention</span></a>
<a class="sourceLine" id="cb2-30" title="30"><span class="co"># that triples (delta = 3) the individual-specific odds of receiving treatment</span></a>
<a class="sourceLine" id="cb2-31" title="31">os_medshift <-<span class="st"> </span><span class="kw">medshift</span>(<span class="dt">W =</span> example_data<span class="op">$</span>W, <span class="dt">A =</span> example_data<span class="op">$</span>A,</a>
<a class="sourceLine" id="cb2-32" title="32"> <span class="dt">Z =</span> example_data<span class="op">$</span>Z, <span class="dt">Y =</span> example_data<span class="op">$</span>Y,</a>
<a class="sourceLine" id="cb2-33" title="33"> <span class="dt">delta =</span> <span class="dv">3</span>, <span class="dt">estimator =</span> <span class="st">"onestep"</span>,</a>
<a class="sourceLine" id="cb2-34" title="34"> <span class="dt">estimator_args =</span> <span class="kw">list</span>(<span class="dt">cv_folds =</span> <span class="dv">3</span>))</a>
<a class="sourceLine" id="cb2-35" title="35"><span class="kw">summary</span>(os_medshift)</a>
<a class="sourceLine" id="cb2-36" title="36"><span class="co">#> lwr_ci param_est upr_ci param_var eif_mean estimator </span></a>
<a class="sourceLine" id="cb2-37" title="37"><span class="co">#> 0.7401 0.788136 0.836172 0.000601 1.64686e-17 onestep</span></a></code></pre></div>
<p>For details on how to use data adaptive regression (machine learning) techniques in the estimation of nuisance parameters, consider consulting the vignette that accompanies this package.</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/medshift/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/medshift/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>medshift</code> R package, please cite the following:</p>
<pre><code> @article{diaz2020causal,
title={Causal mediation analysis for stochastic interventions},
author={D{\'\i}az, Iv{\'a}n and Hejazi, Nima S},
year={2020},
url = {https://doi.org/10.1111/rssb.12362},
doi = {10.1111/rssb.12362},
journal={Journal of the Royal Statistical Society: Series B
(Statistical Methodology)},
volume={},
number={},
pages={},
publisher={Wiley Online Library}
}
@manual{hejazi2020medshift,
author = {Hejazi, Nima S and D{\'\i}az, Iv{\'a}n},
title = {{medshift}: Causal mediation analysis for stochastic
interventions},
year = {2020},
url = {https://github.com/nhejazi/medshift},
note = {R package version 0.1.4}
}
</code></pre>
<hr />
<h2 id="license">License</h2>
<p>© 2018-2022 <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) 2018-2022 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">
<div id="ref-chernozhukov2018double">
<p>Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” <em>The Econometrics Journal</em> 21 (1). <a href="https://doi.org/10.1111/ectj.12097">https://doi.org/10.1111/ectj.12097</a>.</p>
</div>
<div id="ref-coyle-gh-sl3">
<p>Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, Rachael V Phillips, and Oleg Sofrygin. 2022. <em>sl3: Modern Pipelines for Machine Learning and Super Learning</em>. <a href="https://github.com/tlverse/sl3">https://github.com/tlverse/sl3</a>. <a href="https://doi.org/10.5281/zenodo.1342293">https://doi.org/10.5281/zenodo.1342293</a>.</p>
</div>
<div id="ref-diaz2020causal">
<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>. <a href="https://doi.org/10.1111/rssb.12362">https://doi.org/10.1111/rssb.12362</a>.</p>
</div>
<div id="ref-pfanzagl1985contributions">
<p>Pfanzagl, J, and W Wefelmeyer. 1985. “Contributions to a General Asymptotic Statistical Theory.” <em>Statistics & Risk Modeling</em> 3 (3-4): 379–88.</p>
</div>
<div id="ref-vdl2011targeted">
<p>van der Laan, Mark J, and Sherri Rose. 2011. <em>Targeted Learning: Causal Inference for Observational and Experimental Data</em>. Springer Science & Business Media.</p>
</div>
<div id="ref-zheng2011cross">
<p>Zheng, Wenjing, and Mark J van der Laan. 2011. “Cross-Validated Targeted Minimum-Loss-Based Estimation.” In <em>Targeted Learning</em>, 459–74. Springer.</p>
</div>
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