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Robustness and Sensitivity to Modeling Assumptions
Sat, September 7, 2:00 to 3:30pm, Pennsylvania Convention Center (PCC), 106BSession Submission Type: Created Panel
Session Description
The panel offers novel approaches to evaluating robustness of models and designs to violations of their standard assumptions. Covering questions on model specification, variable operationalization, external validity, power calculations, and discontinuities in RDDs, the papers leverage recent advances in econometrics and machine learning to explore the sensitivity of results to researcher decisions and modeling assumptions.
Sub Unit
Chair
Discussants
Individual Presentations
The Sources of Model Uncertainty - Michael Ganslmeier; Tim Vlandas, University of Oxford
Assessing the Robustness of Post-estimation Quantities of Interest - Ian Delabie, University of Pittsburgh; Max Goplerud, University of Texas at Austin
How Much Should You Trust Power Calculations? Power Analyses as Estimations - Shiyao Liu, Peking University; Teppei Yamamoto, Massachusetts Institute of Technology
Confidence in RDDs: Change Detection through Bayesian Change Point Analysis - Bryant James Moy, New York University