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DTSTAMP:20250808T111200Z
DESCRIPTION:Practical Solutions to the Problem of Causal Inference with Err
 or-Prone Covariates.\n\nJ.R. Lockwood is a Principal Research Scientist at
  Educational Testing Service. He specializes in longitudinal modeling of s
 tudent achievement\, the measurement of teaching quality\, and experimenta
 l and quasi-experimental methods in educational evaluation. His recent met
 hodological research interests include measurement error modeling and caus
 al inference with observational data. Prior to joining ETS\, he was a Seni
 or Statistician at the RAND Corporation. He received his Ph.D. in Statisti
 cs from Carnegie Mellon University in 2001. He was the winner of the 2016 
 Causal Effects Data Analysis Challenge sponsored by the Atlantic Causal In
 ference Conference.\n\n	Background variables (covariates) are commonly used
  to adjust for pre-treatment differences between treatment and control gro
 ups in observational evaluations. However\, it is common in settings such 
 as educational and health-related evaluation studies for key covariates to
  be measured with error. For example\, standardized test scores\, often us
 ed to adjust nonequivalent groups in educational evaluations\, measure lat
 ent achievement constructs with error. Such error can lead to bias in trea
 tment effect estimates if unaddressed. This presentation will discuss rece
 nt research on methods that can be used to mitigate the bias due to covari
 ate measurement error in a variety of statistical models commonly used for
  causal inference. It will demonstrate the practical value of Simulation-E
 xtrapolation (SIMEX)\, a simple and intuitive method\, for mitigating bias
  even with complicated causal effect estimators.\n
DTSTART:20171017T193000Z
DTEND:20171017T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:John R. Lockwood\, PhD\, Educational Testing Services (ETS)
URL:/mathstat/channels/event/john-r-lockwood-phd-educa
 tional-testing-services-ets-276754
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