deploy: 6670ad3

Author: TomeHirata

_sources/api_reference.rst.txt

@@ -16,13 +16,13 @@ The dte_adj package provides several types of estimators for computing distribut
 
 For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization and Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization.
 
-For multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2024]_.
+For multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2025]_.
 
 .. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
 
 .. [#car2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
 
-.. [#multitask2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
+.. [#multitask2025] Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
 
 Detailed Documentation
 ----------------------

_sources/index.rst.txt

@@ -29,13 +29,13 @@ For theoretical foundations, see:
 
 * **Simple randomization**: Byambadalai et al. (2024) [#simple2024]_
 * **Covariate-adaptive randomization**: Byambadalai et al. (2025) [#car2025]_
-* **Multi-task learning**: Byambadalai et al. (2024) [#multitask2024]_
+* **Multi-task learning**: Hirata et al. (2025) [#multitask2025]_
 
 .. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
 
 .. [#car2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
 
-.. [#multitask2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
+.. [#multitask2025] Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
 
 .. toctree::
    :maxdepth: 1

api_reference.html

@@ -46,7 +46,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">
 <li><p><strong>Plotting Utilities</strong>: Visualization tools for treatment effects and distributions</p></li>
 </ul>
 <p>For theoretical foundations, see Byambadalai et al. (2024) <a class="footnote-reference brackets" href="#simple2024" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> for simple randomization and Byambadalai et al. (2025) <a class="footnote-reference brackets" href="#car2025" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> for covariate-adaptive randomization.</p>
-<p>For multi-task learning approaches that train models for all locations simultaneously (using <code class="docutils literal notranslate"><span class="pre">is_multi_task=True</span></code>), see the neural network framework in <a class="footnote-reference brackets" href="#multitask2024" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<p>For multi-task learning approaches that train models for all locations simultaneously (using <code class="docutils literal notranslate"><span class="pre">is_multi_task=True</span></code>), see the neural network framework in <a class="footnote-reference brackets" href="#multitask2025" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
 <aside class="footnote-list brackets">
 <aside class="footnote brackets" id="simple2024" role="doc-footnote">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
@@ -56,9 +56,9 @@ <h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
 <p>Byambadalai, U., Hirata, T., Oka, T., &amp; Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2506.05945">arXiv:2506.05945</a>.</p>
 </aside>
-<aside class="footnote brackets" id="multitask2024" role="doc-footnote">
+<aside class="footnote brackets" id="multitask2025" role="doc-footnote">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
-<p>Byambadalai, U., Hirata, T., Oka, T., &amp; Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2507.07738">arXiv:2507.07738</a>.</p>
+<p>Hirata, T., Byambadalai, U., Oka, T., Yasui, S., &amp; Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2507.07738">arXiv:2507.07738</a>.</p>
 </aside>
 </aside>
 </section>

index.html

@@ -55,7 +55,7 @@ <h2>Theoretical Foundations<a class="headerlink" href="#theoretical-foundations"
 <ul class="simple">
 <li><p><strong>Simple randomization</strong>: Byambadalai et al. (2024) <a class="footnote-reference brackets" href="#simple2024" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a></p></li>
 <li><p><strong>Covariate-adaptive randomization</strong>: Byambadalai et al. (2025) <a class="footnote-reference brackets" href="#car2025" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p></li>
-<li><p><strong>Multi-task learning</strong>: Byambadalai et al. (2024) <a class="footnote-reference brackets" href="#multitask2024" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a></p></li>
+<li><p><strong>Multi-task learning</strong>: Hirata et al. (2025) <a class="footnote-reference brackets" href="#multitask2025" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a></p></li>
 </ul>
 <aside class="footnote-list brackets">
 <aside class="footnote brackets" id="simple2024" role="doc-footnote">
@@ -66,9 +66,9 @@ <h2>Theoretical Foundations<a class="headerlink" href="#theoretical-foundations"
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
 <p>Byambadalai, U., Hirata, T., Oka, T., &amp; Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2506.05945">arXiv:2506.05945</a>.</p>
 </aside>
-<aside class="footnote brackets" id="multitask2024" role="doc-footnote">
+<aside class="footnote brackets" id="multitask2025" role="doc-footnote">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
-<p>Byambadalai, U., Hirata, T., Oka, T., &amp; Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2507.07738">arXiv:2507.07738</a>.</p>
+<p>Hirata, T., Byambadalai, U., Oka, T., Yasui, S., &amp; Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint <a class="reference external" href="https://arxiv.org/abs/2507.07738">arXiv:2507.07738</a>.</p>
 </aside>
 </aside>
 <div class="toctree-wrapper compound">