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2 changes: 1 addition & 1 deletion previews/PR109/.documenter-siteinfo.json
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{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-22T14:24:09","documenter_version":"1.4.0"}}
{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-29T15:32:34","documenter_version":"1.4.0"}}
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return (TMLE.estimate(result), lb, ub)
end

make_animation(tmle_inference)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">&quot;tmle_inference.gif&quot;</code></pre><p><img src="../tmle_inference.gif" alt="TMLE Inference"/></p><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../super_learning/">« Becoming a Super Learner</a><a class="docs-footer-nextpage" href="../../estimators_cheatsheet/">Estimators&#39; Cheat Sheet »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 22 April 2024 14:24">Monday 22 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
make_animation(tmle_inference)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">&quot;tmle_inference.gif&quot;</code></pre><p><img src="../tmle_inference.gif" alt="TMLE Inference"/></p><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../super_learning/">« Becoming a Super Learner</a><a class="docs-footer-nextpage" href="../../estimators_cheatsheet/">Estimators&#39; Cheat Sheet »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 29 April 2024 15:32">Monday 29 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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fit!(mach, verbosity=0)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">trained Machine; does not cache data
model: ProbabilisticStack(metalearner = LogisticClassifier(lambda = 2.220446049250313e-16, …), …)
args:
1: Source @287 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @016 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}
1: Source @824 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @427 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}
</code></pre><p>Or evaluated. Because the Stack contains a cross-validation procedure, this will result in two nested levels of resampling.</p><pre><code class="language-julia hljs">evaluate!(mach, measure=log_loss, resampling=resampling)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">PerformanceEvaluation object with these fields:
model, measure, operation, measurement, per_fold,
per_observation, fitted_params_per_fold,
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<span class="sgr36"> 1 column omitted
</span></code></pre><h2 id="Diagnostic"><a class="docs-heading-anchor" href="#Diagnostic">Diagnostic</a><a id="Diagnostic-1"></a><a class="docs-heading-anchor-permalink" href="#Diagnostic" title="Permalink"></a></h2><p>Optionally, one can also investigate how sucessful the weak learners were in the Stack&#39;s internal cross-validation. This is done by specifying the <code>measures</code> keyword argument.</p><p>Here we look at both the Log-Loss and the AUC.</p><pre><code class="language-julia hljs">stack.measures = [log_loss, auc]
fit!(mach, verbosity=0)
report(mach).cv_report</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">(self_tuning_xgboost = PerformanceEvaluation(0.0219, 1.0),
lr = PerformanceEvaluation(0.164, 0.984),
report(mach).cv_report</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">(self_tuning_xgboost = PerformanceEvaluation(0.0197, 1.0),
lr = PerformanceEvaluation(0.166, 0.983),
knn_2 = PerformanceEvaluation(2.22e-16, 1.0),
knn_3 = PerformanceEvaluation(2.22e-16, 1.0),)</code></pre><p>One can look at the fitted parameters for the metalearner as well:</p><pre><code class="language-julia hljs">fitted_params(mach).metalearner</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">(classes = CategoricalArrays.CategoricalValue{Int64, UInt32}[0, 1],
coefs = [:x1 =&gt; -243.60114371771172, :x2 =&gt; 243.60114260352069, :x3 =&gt; -200.00342939296505, :x4 =&gt; 200.00342939296505, :x5 =&gt; -249.9999999999445, :x6 =&gt; 249.9999999999445, :x7 =&gt; -249.9999999999445, :x8 =&gt; 249.9999999999445],
intercept = 0.0,)</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../user_guide/misc/">« Miscellaneous</a><a class="docs-footer-nextpage" href="../double_robustness/">Model Misspecification &amp; Double Robustness »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 22 April 2024 14:24">Monday 22 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
coefs = [:x1 =&gt; -244.8694960176401, :x2 =&gt; 244.8694962486554, :x3 =&gt; -198.4419951378359, :x4 =&gt; 198.44199513783593, :x5 =&gt; -249.9999999999445, :x6 =&gt; 249.9999999999445, :x7 =&gt; -249.9999999999445, :x8 =&gt; 249.9999999999445],
intercept = 0.0,)</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../user_guide/misc/">« Miscellaneous</a><a class="docs-footer-nextpage" href="../double_robustness/">Model Misspecification &amp; Double Robustness »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 29 April 2024 15:32">Monday 29 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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t-statistic: 83.86604877048882
degrees of freedom: 99
empirical standard error: 0.029727653554063295
</code></pre><p>We are comforted to see that our estimator covers the ground truth! 🥳</p><h2 id="Scope-and-Distinguishing-Features"><a class="docs-heading-anchor" href="#Scope-and-Distinguishing-Features">Scope and Distinguishing Features</a><a id="Scope-and-Distinguishing-Features-1"></a><a class="docs-heading-anchor-permalink" href="#Scope-and-Distinguishing-Features" title="Permalink"></a></h2><p>The goal of this package is to provide an entry point for semi-parametric asymptotic unbiased and efficient estimation in Julia. The two main general estimators that are known to achieve these properties are the One-Step estimator and the Targeted Maximum-Likelihood estimator. Most of the current effort as been centered around estimands that are composite of the counterfactual mean.</p><p>Distinguishing Features:</p><ul><li>Estimands: Counterfactual Mean, Average Treatment Effect, Interactions, Any composition thereof</li><li>Estimators: TMLE, One-Step</li><li>Machine-Learning: Any <a href="https://alan-turing-institute.github.io/MLJ.jl/stable/">MLJ</a> compatible model</li><li>Dataset: Any dataset respecting the <a href="https://tables.juliadata.org/stable/">Tables</a> interface (e.g. <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a>)</li><li>Factorial Treatment Variables:<ul><li>Multiple treatments</li><li>Categorical treatment values</li></ul></li></ul><h2 id="Citing-TMLE.jl"><a class="docs-heading-anchor" href="#Citing-TMLE.jl">Citing TMLE.jl</a><a id="Citing-TMLE.jl-1"></a><a class="docs-heading-anchor-permalink" href="#Citing-TMLE.jl" title="Permalink"></a></h2><p>If you use TMLE.jl for your own work and would like to cite us, here are the BibTeX and APA formats:</p><ul><li>BibTeX</li></ul><pre><code class="language-bibtex hljs">@software{Labayle_TMLE_jl,
</code></pre><p>We are comforted to see that our estimator covers the ground truth! 🥳</p><h2 id="Scope-and-Distinguishing-Features"><a class="docs-heading-anchor" href="#Scope-and-Distinguishing-Features">Scope and Distinguishing Features</a><a id="Scope-and-Distinguishing-Features-1"></a><a class="docs-heading-anchor-permalink" href="#Scope-and-Distinguishing-Features" title="Permalink"></a></h2><p>The goal of this package is to provide an entry point for semi-parametric asymptotic unbiased and efficient estimation in Julia. The two main general estimators that are known to achieve these properties are the One-Step estimator and the Targeted Maximum-Likelihood estimator. Most of the current effort has been centered around estimands that are composite of the counterfactual mean.</p><p>Distinguishing Features:</p><ul><li>Estimands: Counterfactual Mean, Average Treatment Effect, Interactions, Any composition thereof</li><li>Estimators: TMLE, One-Step, in both canonical and cross-validated versions.</li><li>Machine-Learning: Any <a href="https://alan-turing-institute.github.io/MLJ.jl/stable/">MLJ</a> compatible model</li><li>Dataset: Any dataset respecting the <a href="https://tables.juliadata.org/stable/">Tables</a> interface (e.g. <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a>)</li><li>Factorial Treatment Variables:<ul><li>Multiple treatments</li><li>Categorical treatment values</li></ul></li></ul><h2 id="Citing-TMLE.jl"><a class="docs-heading-anchor" href="#Citing-TMLE.jl">Citing TMLE.jl</a><a id="Citing-TMLE.jl-1"></a><a class="docs-heading-anchor-permalink" href="#Citing-TMLE.jl" title="Permalink"></a></h2><p>If you use TMLE.jl for your own work and would like to cite us, here are the BibTeX and APA formats:</p><ul><li>BibTeX</li></ul><pre><code class="language-bibtex hljs">@software{Labayle_TMLE_jl,
author = {Labayle, Olivier and Beentjes, Sjoerd and Khamseh, Ava and Ponting, Chris},
title = {{TMLE.jl}},
url = {https://github.com/olivierlabayle/TMLE.jl}
}</code></pre><ul><li>APA</li></ul><p>Labayle, O., Beentjes, S., Khamseh, A., &amp; Ponting, C. TMLE.jl [Computer software]. https://github.com/olivierlabayle/TMLE.jl</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="walk_through/">Walk Through »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 22 April 2024 14:24">Monday 22 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
}</code></pre><ul><li>APA</li></ul><p>Labayle, O., Beentjes, S., Khamseh, A., &amp; Ponting, C. TMLE.jl [Computer software]. https://github.com/olivierlabayle/TMLE.jl</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="walk_through/">Walk Through »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.0 on <span class="colophon-date" title="Monday 29 April 2024 15:32">Monday 29 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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