<div><br></div><div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <span dir="auto"><<a href="mailto:elizavette.torres@yale.edu">elizavette.torres@yale.edu</a>></span><br>Date: Wed, Feb 12, 2020 at 1:17 PM<br>Subject: [Statseminars] S&DS Talk, Speaker: Yixin Wang, 02/17, "The Blessings of Multiple Causes", DL220<br>To: <<a href="mailto:Statseminars@mailman.yale.edu">Statseminars@mailman.yale.edu</a>>, <<a href="mailto:sds-majors@mailman.yale.edu">sds-majors@mailman.yale.edu</a>><br></div><br><br><div lang="EN-US" link="#0563C1" vlink="#954F72"><div class="m_2033078529812886765WordSection1"><p class="MsoNormal" style="background:white"><span style="color:black"><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
" target="_blank"><span style="font-size:20.0pt;font-family:"Lucida Sans",sans-serif;color:#4472c4;text-decoration:none"><img border="0" src="cid:1703aafe630155f77111" alt="Department of Statistics and Data Science
" style="width:159px;max-width:100%"></span></a></span><b><i><span style="font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#2f5597"> Monday, February 17, 2020 – Statistics and Data Science Seminar <u></u><u></u></span></i></b></p><p class="MsoNormal" style="margin-top:.1in"><span style="font-size:24.0pt;font-family:"Times New Roman",serif;color:black">Featured Speaker: Yixin Wang,</span><span style="font-size:13.0pt;font-family:Mallory;color:#222222;background:white"> </span><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black">Columbia University</span><span style="font-size:13.0pt;font-family:Mallory;color:#222222;background:white"><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><u></u><img src="cid:1703aafe630a2d7fd182" align="left" hspace="12" style="width:137px;max-width:100%"><u></u><span style="font-family:"Calibri Light",sans-serif"><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><span style="font-family:"Calibri Light",sans-serif;color:black">DATE:</span><span class="m_2033078529812886765MsoHyperlink"><span style="font-size:13.5pt;font-family:Mallory;color:#003c76"> </span></span><span class="m_2033078529812886765fn"><span style="font-size:13.0pt;color:#222222">Monday, February 17, 2020<u></u><u></u></span></span></p><p class="MsoNormal" style="background:white"><span style="font-family:"Calibri Light",sans-serif;color:black">TIME</span><span class="m_2033078529812886765fn"><span style="font-size:13.0pt;font-family:Mallory;color:#222222">:</span></span><span class="m_2033078529812886765fn"><span style="font-size:13.0pt;color:#222222"> 4:00PM to 5:00PM</span></span><span style="font-size:13.5pt;font-family:Mallory;color:#003c76"><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><span style="font-family:"Calibri Light",sans-serif;color:black">LOCATION</span><span class="m_2033078529812886765fn"><span style="font-size:13.0pt;font-family:Mallory;color:#222222">: Dunham Lab.</span></span><span style="font-size:13.0pt;font-family:Mallory;color:#222222"><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><span style="font-size:13.0pt;font-family:Mallory;color:#222222"><a href="https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g">10 Hillhouse Avenue, Rm. 220</a><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><span class="m_2033078529812886765locality"><span style="font-size:13.0pt;font-family:Mallory;color:#222222"><a href="https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g">New Haven</a></span></span><span style="font-size:13.0pt;font-family:Mallory;color:#222222">, <span class="m_2033078529812886765region"><a href="https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g">CT</a></span> <span class="m_2033078529812886765postal-code"><a href="https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g">06511</a><u></u><u></u></span></span></p><p class="MsoNormal" style="background:white"><span style="color:black"><a href="https://statistics.yale.edu/seminars/yixin-wang" target="_blank">https://statistics.yale.edu/seminars/yixin-wang</a></span><span style="font-size:13.0pt;font-family:Mallory;color:#222222"><u></u><u></u></span></p><p class="MsoNormal" style="background:white"><b><span style="font-size:15.0pt;font-family:"Calibri Light",sans-serif;color:#222222"><u></u> <u></u></span></b></p><p class="MsoNormal" style="background:white"><b><span style="font-size:15.0pt;font-family:Mallory;color:#222222">The Blessings of Multiple Causes<u></u><u></u></span></b></p><p class="MsoNormal" style="background:white"><span style="font-size:13.5pt;font-family:"Calibri Light",sans-serif;color:#222222"><u></u> <u></u></span></p><p style="margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white"><b><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif;color:#222222">Information and Abstract: </span></b><span style="font-size:13.0pt;font-family:Mallory;color:#222222">Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference from observational data allowing for unobserved confounding.<u></u><u></u></span></p><p style="margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing:inherit"><span style="font-size:13.0pt;font-family:Mallory;color:#222222">How does the deconfounder work? The deconfounder is designed for problems of multiple causal inferences: scientific studies that involve many causes whose effects are simultaneously of interest. The deconfounder uses the correlation among causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference. We study the theoretical requirements for the deconfounder to provide unbiased causal estimates, along with its limitations and tradeoffs. We demonstrate the deconfounder on real-world data and simulation studies.<u></u><u></u></span></p><p class="MsoNormal" style="background:white"><b><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif;color:#222222"><u></u> <u></u></span></b></p><p class="MsoNormal" style="line-height:18.0pt;background:white"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif;color:black"><a><span style="color:black;text-decoration:none">3:45 p.m.</span></a> Pre-talk tea Dunham Lab<a href="https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g">,</a> Suite 222, Breakroom 228</span><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif"><u></u><u></u></span></p><p class="MsoNormal" style="background:white;vertical-align:baseline"><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif;color:black">For more details and upcoming events visit our website at <a href="http://statistics.yale.edu/" target="_blank"><span style="color:#0563c1">http://statistics.yale.edu/</span></a> .</span><span style="font-size:12.0pt;font-family:"Calibri Light",sans-serif"><u></u><u></u></span></p><p class="MsoNormal"><span style="font-family:"Calibri Light",sans-serif"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-family:"Calibri Light",sans-serif"><u></u> <u></u></span></p><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal"><u></u> <u></u></p></div></div>_______________________________________________<br>
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