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		<div class="page-header"><h1>TargetedLearning Julia Package</h1></div>
		<div class="well small">Permalink: <a class="more" href="/targetedlearning-julia-package.html">2015-11-22 22:41:00-08:00</a>
by <a class="url fn" href="/author/admin.html">admin </a>
 in <a href="/category/software.html">Software</a>
tags: <a href="/tag/julia.html">Julia</a> <a href="/tag/machine-learning.html">machine learning</a> <a href="/tag/online-learning.html">online learning</a> <a href="/tag/mini-batch.html">mini-batch</a> </div>
		<div><p>Targeted minimum loss-based estimation (TMLE) (sometimes targeted maximum likelihood estimation) is a general framework for constructing regular, asymptotically linear estimators for pathwise differentiable parameters with additional properties such as asymptotic efficiency and double robustness. For background and details, see Targeted Learning by van der Laan and Rose, or articles on TMLE.</p>
<p>The <a href="https://github.com/lendle/TargetedLearning.jl">TargetedLearning.jl</a>  is a package for Julia v0.4.x that implements a TMLE and collaborative TMLE (CTMLE) to estimate a handful of statistical parameters. In particular, we can currently estimate the (statistical parameter corresponding to the) counterfactual mean of some outcome under a static or dynamic single time point treatment with baseline covariates or the difference in counterfactual means. Additionally, arbitrary transformations of these parameters can be estimated, and inference is performed automatically. With a little massaging, the functionality of this package can also be used to estimate the mean of an outcome subject to missingness.</p>
<p><strong>Author(s)</strong>: Sam Lendle, Mark van der Laan</p>
<p><a href="https://github.com/lendle/TargetedLearning.jl">GitHub</a> | <a href="https://lendle.github.io/TargetedLearning.jl/">Website</a></p></div>
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