<div dir="ltr"><span style="font-size:12.8px">Hi everyone!</span><div style="font-size:12.8px"><br></div><div><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif">This week at the<span class="gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space"> </span></span>Applied Statistics <span style="line-height:14.72px"><font face="Arial, sans-serif"><span style="font-size:10pt">Worksho<wbr>p we will be welcoming</span></font><span class="gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"> </span><b style="font-family:Arial,sans-serif;font-size:10pt"><i>Molly Roberts</i></b><font face="Arial, sans-serif"><span style="font-size:10pt">, Assistant Professor at the University of California, San Diego. She will be presenting work entitled</span></font><b style="font-family:Arial,sans-serif;font-size:10pt"><i> </i></b><font face="Arial, sans-serif"><span style="font-size:13.3333px"><b><i>How to Make Causal Inferences Using Text</i></b></span><span style="font-size:10pt">.  Please find the abstract below and on the Applied Stats website <a href="https://projects.iq.harvard.edu/applied.stats.workshop-gov3009" target="_blank">here</a>.</span></font></span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif"> </span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif">As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be provided.  See you all there!</span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif"> </span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif">-- Dana Higgins</span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif"> </span></p><p class="MsoNormal"><span style="line-height:15.3333px"><br><b style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Title:</span></b><span class="gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"> </span></span><u style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">How to Make Causal Inferences Using Text</span></u><br>(with Naoki Egami, Christian Fong, Justin Grimmer and Brandon Stewart)</span></p><p class="MsoNormal"><span style="line-height:15.3333px"><br><b style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Abstract:</span></b><span class="gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"> </span></span></span>Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome.  We introduce a new conceptual framework to understand all text-based causal inferences, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. Estimating this latent representation, however, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences, connecting two previous disparate literatures.</p></div><br>
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