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@@ -197,7 +197,7 @@ <h2 class="anchored" data-anchor-id="schedule-for-fall-2024">Schedule for Fall 2
 <tr class="even">
 <td>4<sup>th</sup> November (Week 4)</td>
 <td>Semi-parametric estimation in longitudinal causal inference</td>
-<td><span class="citation" data-cites="bang2005doubly">Bang and Robins (<a href="#ref-bang2005doubly" role="doc-biblioref">2005</a>)</span></td>
+<td><span class="citation" data-cites="bang2005doubly">Bang and Robins (<a href="#ref-bang2005doubly" role="doc-biblioref">2005</a>)</span> and <span class="citation" data-cites="van2011targeted">Van Der Laan and Gruber (<a href="#ref-van2011targeted" role="doc-biblioref">2011</a>)</span></td>
 <td>CJ</td>
 </tr>
 <tr class="odd">
@@ -238,6 +238,9 @@ <h2 class="anchored" data-anchor-id="schedule-for-fall-2024">Schedule for Fall 2
 <div id="ref-rytgaard2020phd" class="csl-entry" role="listitem">
 Rytgaard, Helene Charlotte. 2020. <span>“Targeted Causal Learning for Longitudinal Data.”</span> PhD thesis, University of Copenhagen. <a href="https://biostat.ku.dk/dissertations/2020_rytgaard.pdf">https://biostat.ku.dk/dissertations/2020_rytgaard.pdf</a>.
 </div>
+<div id="ref-van2011targeted" class="csl-entry" role="listitem">
+Van Der Laan, Mark J., and Susan Gruber. 2011. <span>“Targeted Minimum Loss-Based Estimation of an Intervention-Specific Mean Outcome.”</span> Technical Report 290. University of California, Berkeley, Division of Biostatistics. <a href="https://biostats.bepress.com/ucbbiostat/paper290/">https://biostats.bepress.com/ucbbiostat/paper290/</a>.
+</div>
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refs.html

@@ -190,6 +190,9 @@ <h1 class="title">References</h1>
 <div id="ref-tsiatis2007semiparametric" class="csl-entry" role="listitem">
 Tsiatis, Anastasios. 2007. <em>Semiparametric Theory and Missing Data</em>. Springer. <a href="https://doi.org/10.1007/0-387-37345-4">https://doi.org/10.1007/0-387-37345-4</a>.
 </div>
+<div id="ref-van2011targeted" class="csl-entry" role="listitem">
+Van Der Laan, Mark J., and Susan Gruber. 2011. <span>“Targeted Minimum Loss-Based Estimation of an Intervention-Specific Mean Outcome.”</span> Technical Report 290. University of California, Berkeley, Division of Biostatistics. <a href="https://biostats.bepress.com/ucbbiostat/paper290/">https://biostats.bepress.com/ucbbiostat/paper290/</a>.
+</div>
 <div id="ref-vdl2003unified" class="csl-entry" role="listitem">
 van der Laan, Mark J, and James M Robins. 2003. <em>Unified Methods for Censored Longitudinal Data and Causality</em>. Springer. <a href="https://doi.org/10.1007/978-0-387-21700-0">https://doi.org/10.1007/978-0-387-21700-0</a>.
 </div>

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-    "text": "Bang, Heejung, and James M. Robins. 2005. “Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61 (4): 962–73. https://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2005.00377.x.\n\n\nDı́az, Iván. 2020. “Machine Learning in the Estimation of Causal Effects: Targeted Minimum Loss-Based Estimation and Double/Debiased Machine Learning.” Biostatistics 21 (2): 353–58. https://doi.org/10.1093/biostatistics/kxz042.\n\n\nFisher, Aaron, and Edward H Kennedy. 2020. “Visually Communicating and Teaching Intuition for Influence Functions.” The American Statistician 75 (2): 162–72. https://doi.org/10.1080/00031305.2020.1717620.\n\n\nHenmi, Masayuki, and Shinto Eguchi. 2004. “A Paradox Concerning Nuisance Parameters and Projected Estimating Functions.” Biometrika 91 (4): 929–41. https://doi.org/10.1093/biomet/91.4.929.\n\n\nHernán, Miguel A, and James M Robins. 2024. Causal Inference: What If. CRC Press.\n\n\nHines, Oliver, Oliver Dukes, Karla Diaz-Ordaz, and Stijn Vansteelandt. 2022. “Demystifying Statistical Learning Based on Efficient Influence Functions.” The American Statistician 76 (3): 292–304. https://doi.org/10.1080/00031305.2021.2021984.\n\n\nKennedy, Edward H. 2016. “Semiparametric Theory and Empirical Processes in Causal Inference.” In Statistical Causal Inferences and Their Applications in Public Health Research, edited by Hua He, Pan Wu, and Ding-Geng (Din) Chen, 141–67. Springer. https://doi.org/10.1007/978-3-319-41259-7_8.\n\n\n———. 2022. “Semiparametric Doubly Robust Targeted Double Machine Learning: A Review.” arXiv Preprint arXiv:2203.06469. https://doi.org/10.48550/arXiv.2203.06469.\n\n\nKosorok, Michael R. 2008. Introduction to Empirical Processes and Semiparametric Inference. Springer. https://doi.org/10.1007/978-0-387-74978-5.\n\n\nRytgaard, Helene Charlotte. 2020. “Targeted Causal Learning for Longitudinal Data.” PhD thesis, University of Copenhagen. https://biostat.ku.dk/dissertations/2020_rytgaard.pdf.\n\n\nTsiatis, Anastasios. 2007. Semiparametric Theory and Missing Data. Springer. https://doi.org/10.1007/0-387-37345-4.\n\n\nvan der Laan, Mark J, and James M Robins. 2003. Unified Methods for Censored Longitudinal Data and Causality. Springer. https://doi.org/10.1007/978-0-387-21700-0.\n\n\nvan der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer. https://doi.org/10.1007/978-1-4419-9782-1.\n\n\n———. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer. https://doi.org/10.1007/978-3-319-65304-4.\n\n\nYing, Andrew. 2024. “A Geometric Perspective on Double Robustness by Semiparametric Theory and Information Geometry.” arXiv Preprint arXiv:2404.13960. https://arxiv.org/abs/2404.13960."
+    "text": "Bang, Heejung, and James M. Robins. 2005. “Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61 (4): 962–73. https://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2005.00377.x.\n\n\nDı́az, Iván. 2020. “Machine Learning in the Estimation of Causal Effects: Targeted Minimum Loss-Based Estimation and Double/Debiased Machine Learning.” Biostatistics 21 (2): 353–58. https://doi.org/10.1093/biostatistics/kxz042.\n\n\nFisher, Aaron, and Edward H Kennedy. 2020. “Visually Communicating and Teaching Intuition for Influence Functions.” The American Statistician 75 (2): 162–72. https://doi.org/10.1080/00031305.2020.1717620.\n\n\nHenmi, Masayuki, and Shinto Eguchi. 2004. “A Paradox Concerning Nuisance Parameters and Projected Estimating Functions.” Biometrika 91 (4): 929–41. https://doi.org/10.1093/biomet/91.4.929.\n\n\nHernán, Miguel A, and James M Robins. 2024. Causal Inference: What If. CRC Press.\n\n\nHines, Oliver, Oliver Dukes, Karla Diaz-Ordaz, and Stijn Vansteelandt. 2022. “Demystifying Statistical Learning Based on Efficient Influence Functions.” The American Statistician 76 (3): 292–304. https://doi.org/10.1080/00031305.2021.2021984.\n\n\nKennedy, Edward H. 2016. “Semiparametric Theory and Empirical Processes in Causal Inference.” In Statistical Causal Inferences and Their Applications in Public Health Research, edited by Hua He, Pan Wu, and Ding-Geng (Din) Chen, 141–67. Springer. https://doi.org/10.1007/978-3-319-41259-7_8.\n\n\n———. 2022. “Semiparametric Doubly Robust Targeted Double Machine Learning: A Review.” arXiv Preprint arXiv:2203.06469. https://doi.org/10.48550/arXiv.2203.06469.\n\n\nKosorok, Michael R. 2008. Introduction to Empirical Processes and Semiparametric Inference. Springer. https://doi.org/10.1007/978-0-387-74978-5.\n\n\nRytgaard, Helene Charlotte. 2020. “Targeted Causal Learning for Longitudinal Data.” PhD thesis, University of Copenhagen. https://biostat.ku.dk/dissertations/2020_rytgaard.pdf.\n\n\nTsiatis, Anastasios. 2007. Semiparametric Theory and Missing Data. Springer. https://doi.org/10.1007/0-387-37345-4.\n\n\nVan Der Laan, Mark J., and Susan Gruber. 2011. “Targeted Minimum Loss-Based Estimation of an Intervention-Specific Mean Outcome.” Technical Report 290. University of California, Berkeley, Division of Biostatistics. https://biostats.bepress.com/ucbbiostat/paper290/.\n\n\nvan der Laan, Mark J, and James M Robins. 2003. Unified Methods for Censored Longitudinal Data and Causality. Springer. https://doi.org/10.1007/978-0-387-21700-0.\n\n\nvan der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer. https://doi.org/10.1007/978-1-4419-9782-1.\n\n\n———. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer. https://doi.org/10.1007/978-3-319-65304-4.\n\n\nYing, Andrew. 2024. “A Geometric Perspective on Double Robustness by Semiparametric Theory and Information Geometry.” arXiv Preprint arXiv:2404.13960. https://arxiv.org/abs/2404.13960."
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-    "text": "All meetings are on Mondays 2-3pm ET and will be held in person only in HSPH 2-426.\n\n\n\n\n\n\n\n\nDate\nTopic\nPaper\nPresenter\n\n\n\n\n23rd September (Week 1)\nIntroduction to longitudinal causal infernece\nCh. 2 of Rytgaard (2020)\nNSH\n\n\n7th October (Week 2)\nOverview of semi-parametric estimation in causal inference\nfinish Ch. 2 of Rytgaard (2020), Ch. 3.1 and 3.2 of Rytgaard (2020) and related from Kennedy (2016)\nSVB\n\n\n21st October (Week 3)\nBuilding blocks of longitudinal causal inference\nCh. 19-21 of Hernán and Robins (2024)\nCT\n\n\n4th November (Week 4)\nSemi-parametric estimation in longitudinal causal inference\nBang and Robins (2005)\nCJ\n\n\n18th November (Week 5)\nTBD\nTBA\n\n\n\n2nd December (Week 6)\nTBD\nTBA\n\n\n\n16th December (Week 7)\nTBD\nTBA"
+    "text": "All meetings are on Mondays 2-3pm ET and will be held in person only in HSPH 2-426.\n\n\n\n\n\n\n\n\nDate\nTopic\nPaper\nPresenter\n\n\n\n\n23rd September (Week 1)\nIntroduction to longitudinal causal infernece\nCh. 2 of Rytgaard (2020)\nNSH\n\n\n7th October (Week 2)\nOverview of semi-parametric estimation in causal inference\nfinish Ch. 2 of Rytgaard (2020), Ch. 3.1 and 3.2 of Rytgaard (2020) and related from Kennedy (2016)\nSVB\n\n\n21st October (Week 3)\nBuilding blocks of longitudinal causal inference\nCh. 19-21 of Hernán and Robins (2024)\nCT\n\n\n4th November (Week 4)\nSemi-parametric estimation in longitudinal causal inference\nBang and Robins (2005) and Van Der Laan and Gruber (2011)\nCJ\n\n\n18th November (Week 5)\nTBD\nTBA\n\n\n\n2nd December (Week 6)\nTBD\nTBA\n\n\n\n16th December (Week 7)\nTBD\nTBA"
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+    "text": "All meetings are on Mondays 2-3pm ET and will be held in person only in HSPH 2-426.\n\n\n\n\n\n\n\n\nDate\nTopic\nPaper\nPresenter\n\n\n\n\n23rd September (Week 1)\nIntroduction to longitudinal causal infernece\nCh. 2 of Rytgaard (2020)\nNSH\n\n\n7th October (Week 2)\nOverview of semi-parametric estimation in causal inference\nfinish Ch. 2 of Rytgaard (2020), Ch. 3.1 and 3.2 of Rytgaard (2020) and related from Kennedy (2016)\nSVB\n\n\n21st October (Week 3)\nBuilding blocks of longitudinal causal inference\nCh. 19-21 of Hernán and Robins (2024)\nCT\n\n\n4th November (Week 4)\nSemi-parametric estimation in longitudinal causal inference\nBang and Robins (2005) and Van Der Laan and Gruber (2011)\nCJ\n\n\n18th November (Week 5)\nTBD\nTBA\n\n\n\n2nd December (Week 6)\nTBD\nTBA\n\n\n\n16th December (Week 7)\nTBD\nTBA"
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