<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div dir="ltr"><meta http-equiv="content-type" content="text/html; charset=utf-8"><div dir="ltr"><meta http-equiv="content-type" content="text/html; charset=utf-8"><br><br><blockquote type="cite"><div dir="ltr"><blockquote type="cite"><div dir="ltr"><b><br></b></div></blockquote><blockquote type="cite"><div dir="ltr"><div class="WordSection1"><div align="center"><table class="MsoNormalTable" border="0" cellspacing="0" cellpadding="0" width="600" style="width:450.0pt"><tbody><tr><td valign="top" style="background:white;padding:0cm 0cm 0cm 0cm"><div align="center"><br></div><div align="center"><table class="MsoNormalTable" border="0" cellspacing="0" cellpadding="0" width="600" style="width:450.0pt"><tbody><tr><td style="background:white;padding:0cm 0cm 0cm 0cm"><table class="MsoNormalTable" border="0" cellspacing="0" cellpadding="0" width="100%" style="width:100.0%"><tbody><tr><td style="padding:0cm 0cm 0cm 0cm"><div align="center"><table class="MsoNormalTable" border="0" cellspacing="0" cellpadding="0" width="540" style="width:405.0pt"><tbody><tr style="height:22.5pt"><td style="padding:0cm 0cm 0cm 0cm;height:22.5pt"><p class="MsoNormal" style="text-autospace:none"><b><span lang="EN-US" style="font-size:18.0pt;font-family:"Verdana","sans-serif";color:#C00026;mso-fareast-language:PT-BR">Data Science<o:p></o:p></span></b></p>
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<p class="MsoNormal" style="line-height:105%"><span lang="EN-US" style="font-family:"Verdana","sans-serif";color:#C00026">Academic Seminar</span><span style="font-size:12.0pt;line-height:105%"><o:p></o:p></span></p>
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<p class="MsoNormal" style="text-autospace:none"><b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333">Title:
</span></b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333">An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?<b><o:p></o:p></b></span></p>
<p class="MsoNormal" style="text-autospace:none"><b><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif""> </span></b><span style="font-size:12.0pt;font-family:"Arial","sans-serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333">Speaker:</span></b><b><span lang="EN-US" style="font-size:12.0pt;font-family:""Calibri"","sans-serif"">
</span></b><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif""><a href="https://www.csail.mit.edu/person/tamara-broderick">Tamara Broderick</a><br>
</span><b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333">University:</span></b><span lang="EN-US" style="font-size:12.0pt;font-family:"Arial","sans-serif"">
</span><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif""><a href="https://web.mit.edu/">Massachusetts Institute of Technology</a></span><span style="font-size:12.0pt;font-family:"Arial","sans-serif""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif";color:#767171"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif";color:#767171">One hopes that data analyses will be used to make beneficial decisions regarding people's health, finances, and well-being.
But the data fed to an analysis may systematically differ from the data where these decisions are ultimately applied. For instance, suppose we analyze data in one country and conclude that microcredit is effective at alleviating poverty; based on this analysis,
we decide to distribute microcredit in other locations and in future years. We might then ask: can we trust our conclusion to apply under new conditions? If we found that a very small percentage of the original data was instrumental in determining the original
conclusion, we might expect the conclusion to be unstable under new conditions. So we propose a method to assess the sensitivity of data analyses to the removal of a small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally
prohibitive, so we provide an approximation. We call our resulting metric the Approximate Maximum Influence Perturbation. Our approximation is automatically computable and works for common estimators --- including (but not limited to) OLS, IV, GMM, MLE, MAP,
and variational Bayes. We show that any non-robustness our metric finds is conclusive. Empirics demonstrate that while some applications are robust, in others the sign of a treatment effect can be changed by dropping much less than 1% of the data --- even
in simple models and even when standard errors are small.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif";color:#767171"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span lang="EN-US" style="font-size:12.0pt;font-family:""Verdana"","sans-serif";color:#767171"><a href="https://arxiv.org/abs/2011.14999">Paper available here.</a></span><span style="font-size:12.0pt"><o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:105%"><span style="font-size:10.5pt;line-height:105%;font-family:"Verdana","sans-serif";color:black">August/05/2021</span><o:p></o:p></p>
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<p class="MsoNormal" style="line-height:105%"><span style="font-size:10.5pt;line-height:105%;font-family:"Verdana","sans-serif";color:black">12pm de São Paulo, Brasil (UTC/GMT -03:00)</span><o:p></o:p></p>
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<p class="MsoNormal"><b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333;mso-fareast-language:PT-BR">#WeAreInsper<o:p></o:p></span></b></p>
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<p class="MsoNormal"><span lang="EN" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#333333;mso-fareast-language:PT-BR">Find upcoming seminars
</span><a href="https://www.insper.edu.br/pesquisa-e-conhecimento/seminarios-academicos/"><b><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#C00026;mso-fareast-language:PT-BR">here</span></b></a><b><u><span lang="EN-US" style="font-size:11.5pt;font-family:"Verdana","sans-serif";color:#C00026;mso-fareast-language:PT-BR">.<o:p></o:p></span></u></b></p>
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<p class="MsoNormal" style="line-height:105%"><span style="font-size:9.0pt;line-height:105%;font-family:"Verdana","sans-serif";color:#7B7B7B">Rua Quatá, 300 - Vila Olímpia<br>
São Paulo/SP - CEP: 04546-042</span><span style="font-size:11.5pt;line-height:105%;font-family:"Verdana","sans-serif";color:#7B7B7B"><o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:105%"><span style="font-size:9.0pt;line-height:105%;font-family:"Verdana","sans-serif";color:#7B7B7B">(11) 4504-2400<br>
<a href="mailto:seminariosacademicos@insper.edu.brsubject=Contato"><span style="color:#F25560">seminariosacademicos@insper.edu.br</span></a></span><span style="font-size:11.5pt;line-height:105%;font-family:"Verdana","sans-serif";color:#7B7B7B"><o:p></o:p></span></p>
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