BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.3.1//EN
TZID:America/Los_Angeles
X-WR-TIMEZONE:America/Los_Angeles
BEGIN:VEVENT
UID:357@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20260408T163000
DTEND;TZID=America/Los_Angeles:20260408T172000
DTSTAMP:20260407T185234Z
URL:https://escience.washington.edu/events/uw-data-science-seminar-hernan-
 querbes-2/
SUMMARY:UW Data Science Seminar: Juejue Wang
DESCRIPTION:Please join us for a UW Data Science Seminar featuring UW Stati
 stics Ph.D. student Juejue Wang on Wednesday\, April 8th from 4:30 to 5:2
 0 p.m. PT. The seminar will be held in IEB G109.\n"Omitted Variable Bias i
 n Difference-in-Differences Designs"\nAbstract: We study the omitted-varia
 ble bias (OVB) problem in canonical difference-in-differences (DiD) design
 s when unobserved confounding induces departures from the parallel trends 
 assumption. Our results provide a novel characterization of the OVB formul
 a for the average treatment effect on the treated (ATT)\, which may be of 
 independent interest. We show how the ATT bias is governed by the strength
  of confounding in the treatment-selection mechanism and provide alternati
 ve ways of quantifying this strength\, such as (i) changes in the average 
 odds of treatment among the treated\, (ii) confounding imbalance between t
 reated and control units\, or (iii) variation explained in treatment odds 
 among the untreated. We additionally consider DiD designs using linear reg
 ressions with two-way fixed effects and show how the OVB simplifies in suc
 h settings. Building on these results\, we offer sensitivity statistics fo
 r routine reporting\, describing the minimum strength of confounding requi
 red to overturn the conclusions of a DiD study\, as well as formal bounds 
 on the strength of confounders based on comparisons to observed covariates
 . Finally\, we provide flexible and efficient statistical inference method
 s for the bounds on ATT\, which can leverage modern machine learning algor
 ithms for estimation. We demonstrate the utility of our approach in two em
 pirical examples.\n\nSpeaker Bio: Juejue Wang is a fourth-year Ph.D. stude
 nt in Statistics at the University of Washington\, advised by Professor Ca
 rlos Cinelli. Her research focuses on sensitivity analysis in causal infer
 ence\, with applications to difference-in-differences designs and instrume
 ntal variables.\n\n\nThe 2025-2026 seminars will be held in person\, and a
 re free and open to the public.\n\n
ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa
 ds/2026/04/IMG_5344-e1775587258429.jpeg
END:VEVENT
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
DTSTART:20260308T030000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
END:DAYLIGHT
END:VTIMEZONE
END:VCALENDAR