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"></span><span style='font-family:"Microsoft YaHei Light",sans-serif;color:#073763;mso-fareast-language:JA'> <a href="https://statistics.yale.edu/" target="_blank" title=Home><span style='font-size:22.0pt;color:#0563C1'>Department of Statistics and Data Science </span></a></span><i><span style='font-size:22.0pt;font-family:"Microsoft YaHei Light",sans-serif;color:#073763;mso-fareast-language:JA'> </span></i><span style='font-family:"Microsoft YaHei Light",sans-serif;mso-fareast-language:JA'><o:p></o:p></span></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in;margin-bottom:.0001pt;background:white'><span style='font-size:14.0pt;font-family:Mallory;color:#003C76;text-transform:uppercase;font-weight:normal'>ERIC J. TCHETGEN TCHETGEN</span><span style='font-size:14.0pt;font-family:Mallory;color:#222222'>, </span><span class=odd><span style='font-size:14.0pt;font-family:Mallory;color:#222222'>The Wharton School, University of Pennsylvania</span></span><span style='font-size:14.0pt;font-family:Mallory;color:#003C76;text-transform:uppercase;font-weight:normal'><o:p></o:p></span></h1><p class=MsoNormal style='background:white'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=95 height=114 style='width:.9895in;height:1.1875in' src="cid:image004.jpg@01D72540.58C50300" align=left hspace=12 alt="https://statistics.yale.edu/sites/default/files/styles/user_picture_node/public/tchetgen_3-129x139_0.jpg?itok=nJeTXlIr" v:shapes="Picture_x0020_3"><![endif]><span class=date-display-single><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'>Date: Friday, April 02, 2021<o:p></o:p></span></span></p><p class=MsoNormal style='background:white'><span class=date-display-single><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'>Time: </span></span><span class=date-display-start><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'>11:30AM</span></span><span class=date-display-range><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'> to </span></span><span class=date-display-end><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'>12:30PM</span></span><span style='font-size:12.0pt;font-family:Mallory;color:#003C76'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Via Zoom: https://yale.zoom.us/j/92708961744<o:p></o:p></span></p><p class=MsoNormal style='background:white'><span class=locality><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Password: 24</span></span><span style='font-size:12.0pt;font-family:Mallory;color:#222222'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:12.0pt;font-family:Mallory;color:#222222'><a href="https://statistics.wharton.upenn.edu/profile/ett/"><span style='color:#003C76'>Website</span></a><b> <o:p></o:p></b></span></p><p class=MsoNormal style='background:white'><b><span style='font-size:12.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Title: Semiparametric Proximal Causal Inference<o:p></o:p></span></b></p><p class=MsoNormal style='background:white'><span style='font-size:12.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></p><p class=MsoNormal style='background:white'><b><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Information and Abstract: <o:p></o:p></span></b></p><p style='margin:0in;margin-bottom:.0001pt;background:white;box-sizing: inherit'><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator’s ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this paper, we consider the framework of proximal causal inference introduced by Tchetgen Tchetgen et al (2020), which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. We make a number of contributions to proximal inference including (i) an alternative set of conditions for nonparametric proximal identification of the average treatment effect; (ii) general semiparametric theory for proximal estimation of the average treatment effect including efficiency bounds for key semiparametric models of interest; (iii) a characterization of proximal doubly robust and locally efficient estimators of the average treatment effect. Moreover, we provide analogous identification and efficiency results for the average treatment effect on the treated. Our approach is illustrated via simulation studies and a data application on evaluating the effectiveness of right heart catheterization in the intensive care unit of critically ill patients.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;mso-fareast-language:JA'><o:p> </o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>