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<!DOCTYPE html>
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<title>Vitor Hadad</title>
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<p>
Vitor Hadad<br />
Applied Scientist (L5)<br />
Amazon Lab126 <br />
<span class="tooltipped tooltipped-e tooltipped-no-delay email pseudo-link"
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<p>
I'm an applied scientist at <a href="https://amazon.jobs/en/teams/lab126/">Amazon Lab126</a>, where I use machine learning-based causal inference and prediction methods to solve problems related to Amazon devices.
Recently I've worked on sleep-related devices such as the Amazon <a href="https://www.amazon.science/blog/amazon-halo-rise-advances-the-future-of-sleep">Halo Rise</a>.
</p>
<p>
Prior to this position, I was a postdoc at the <a href="https://www.gsb.stanford.edu/faculty-research/centers-initiatives/sil">Golub Capital Social Impact Lab</a>, led by <a href="https://www.gsb.stanford.edu/faculty-research/faculty/susan-athey"> Susan Athey</a> at Stanford GSB.
My research focused on the design and analysis of adaptive experiments such as multi-armed bandits and contextual bandits. I was a developer on the R package <a href="https://github.com/grf-labs/grf">grf</a> for nonparametric causal inference based on random forests. </p>
<p> I hold a PhD in Economics from <a href="https://www.bc.edu/">Boston College</a>. My advisors were <a href="http://economics.emory.edu/home/people/faculty/biography/hoderlein-stefan.html">Stefan Hoderlein</a>, <a href="https://www.bc.edu/bc-web/schools/mcas/departments/economics/people/faculty-directory/arthur-lewbel.html">Arthur Lewbel</a> and <a href="https://sites.bc.edu/utku-unver/">Utku Ünver</a>.
</p>
</div>
<div class="row">
<h5> Research </h5>
<ul>
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning (2022).<br/>
Susan Athey, Undral Byambadalai, <b>Vitor Hadad</b>, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams [alphabetically].<br />
Preprint. <br />
<a href="https://arxiv.org/abs/2211.12004">[arXiv]</a>
<a href="https://github.com/grf-labs/sufrep">[github]</a>
</ul>
<ul>
Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives? (2021)<br />
Susan Athey, Katy Bergstrom, <b>Vitor Hadad</b>, Julian C. Jamison, Berk Özler, Luca Parisotto, Julius Dohbit Sama [alphabetically].<br />
Preprint. <br />
<a href="https://www.gsb.stanford.edu/faculty-research/working-papers/shared-decision-making-can-improved-counseling-increase-willingness">[preprint]</a>
</ul>
<ul>
Confidence Intervals for Policy Evaluation in Adaptive Experiments (2021).<br/>
<b>Vitor Hadad</b>, David A. Hirshberg, Ruohan Zhan, Stefan Wager and Susan Athey.<br>
Proceedings of the National Academy of Sciences.<br />
<a href="https://arxiv.org/abs/1911.02768">[arXiv]</a>
<a href="https://github.com/gsbDBI/mab_inference">[github]</a>
</ul>
<ul>
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits (2021).<br/>
Ruohan Zhan, David A. Hirshberg, <b>Vitor Hadad</b> and Susan Athey.<br/>
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. <br />
<a href="https://arxiv.org/abs/2106.02029">[arXiv]</a>
<a href="https://github.com/gsbDBI/contextual_bandits_evaluation">[github]</a>
</ul>
<ul>
Extending Tractable Contextual Bandits Beyond Realizability (2021).<br/>
Sanath Kumar Krishnamurthy, <b>Vitor Hadad</b> and Susan Athey.<br />
International Conference on Machine Learning (ICML).<br />
<a href="https://arxiv.org/abs/2102.13240">[arXiv]</a>
</ul>
<ul>
Tractable Contextual Bandits Beyond Realizability (AISTATS Poster, 2021).<br/>
Sanath Kumar Krishnamurthy, <b>Vitor Hadad</b> and Susan Athey.<br />
International Conference on Artificial Intelligence and Statistics (AISTATS). <br />
<a href="https://arxiv.org/abs/2010.13013">[arXiv]</a>
</ul>
<ul>
Sufficient Representations for Categorical Variables (2018).<br/>
Jonathan Johannemann, <b>Vitor Hadad</b>, Susan Athey and Stefan Wager.<br />
Preprint.<br/>
<a href="https://arxiv.org/abs/1908.09874">[arXiv]</a>
<a href="https://github.com/grf-labs/sufrep">[github]</a>
</ul>
<ul>
Essays in Econometrics and Dynamic Kidney Exchange (2018). <br />
Vitor Hadad. <br /> Doctoral dissertation. <br/>
<a href="https://dlib.bc.edu/islandora/object/bc-ir%3A107962/datastream/PDF/download/citation.pdf">[PDF]</a>
</ul>
</div>
<div class="row">
<h5> Software </h5>
<ul>
<code>GRF</code> Causal inference based on generalized random forests (R/C++ package).<br/>
Based on Wager, Tibshirani and Athey (2019, Ann. Stat.) and others.
<br />
<a href="https://github.com/grf-labs/grf">[github]</a>
<a href="https://grf-labs.github.io/grf/">[docs]</a>
</ul>
<ul>
<code>sufrep</code> Encoding categorical variables as continuous to ML algorithms via sufficient representations (R package). <br/>
Based on Johannemann, Hadad, Athey, Wager (2018).
<br />
<a href="https://github.com/grf-labs/sufrep/">[github]</a>
</ul>
<ul>
<code>fhhps</code> Linear panel data with two endogenous random coefficients (Python package). <br/>
Based on Fox, Hadad, Hoderlein, Petrin and Sherman (In progress).
<br />
<a href="https://github.com/halflearned/FHHPS">[github]</a>
<a href="https://econ2017.sites.olt.ubc.ca/files/2019/03/pdf_seminar_Productivity-Slides_Fox.pdf">[slides]</a>
</ul>
</div>
<div class="row">
<h5> Teaching </h5>
<ul>
Big Data - Boston College EC3389.<br/>
Co-instructor, with Stefan Hoderlein. Spring 2016. <br/>
<a href="https://github.com/halflearned/EC3389">[github]</a>
</ul>
<ul>
Statistics - Boston College EC1151. <br/>
Instructor. Fall 2016, Spring 2017. <br/>
<a href="https://github.com/halflearned/EC1151">[github]</a>
</ul>
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