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DTSTAMP:20251122T072801Z
DESCRIPTION:Automated Inference on Sharp Bounds\n\n\n	Abstract:\n\n\nMany ca
 usal parameters involving the joint distribution of potential outcomes in 
 treated and control states cannot be point-identified\, but only be bounde
 d from above and below. The bounds can be further tightened by conditionin
 g on pre-treatment covariates\, and the sharp version of the bounds corres
 ponds to using a full covariate vector. This paper gives a method for esti
 mation and inference on sharp bounds determined by a linear system of unde
 r-identified equalities (e.g.\, as in Heckman et al (ReSTUD\, 1997)). In t
 he sharp bounds’ case\, the RHS of this system involves a nuisance functio
 n of (many) covariates (e.g.\, the conditional probability of employment i
 n treated or control state). Combining Neyman-orthogonality and sample spl
 itting\, I provide an asymptotically Gaussian estimator of sharp bound tha
 t does not require solving the linear system in closed form. I demonstrate
  the method in an empirical application to Connecticut’s Jobs First welfar
 e reform experiment.\n\n\n	Speaker\n\n\nVira Semenova is an assistant profe
 ssor at UC Berkeley’s Department of Economics. Her research interests are 
 Econometrics and Machine Learning. https://sites.google.com/view/semenovav
 ira\n\nɬÀï·¬ Statistics Seminar schedule: https://mcgillstat.github.io/\n
 \nZoom link: https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNI
 cWF5d0dJQT09\n\nMeeting ID: 834 3668 6293\n\nPasscode: 12345\n\n \n\n \n
DTSTART:20221111T203000Z
DTEND:20221111T213000Z
SUMMARY:Vira Semenova (UC Berkeley)
URL:/mathstat/channels/event/vira-semenova-uc-berkeley
 -343450
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