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CC = Colorado Convention Center   H = Hyatt Regency Denver at Colorado Convention Center
* = applied session       ! = JSM meeting theme

Activity Details

391 * ! Tue, 7/30/2019, 2:00 PM - 3:50 PM CC-106
Leveraging Disparate Sources of Data and Machine Learning to Improve Causal Inference — Topic Contributed Papers
ENAR, Section on Statistical Learning and Data Science, Social Statistics Section
Organizer(s): Johann A Gagnon-Bartsch, University of Michigan; Jann Spiess, Postdoctoral Research, Microsoft Research
Chair(s): Johann A Gagnon-Bartsch, University of Michigan
2:05 PM Transfer Learning for Estimating Causal Effects Using Neural Networks
Soeren Kuenzel; Jasjeet Sekhon, UC Berkeley; Bradly Reinhold Stadie, UC Berkeley; Nikita Vemuri, UC Berkeley
2:25 PM ReLOOP: Precise Unbiased Estimation in Randomized Experiments Using Observational Auxilliary Data

Adam Sales, University of Texas At Austin; Johann A Gagnon-Bartsch, University of Michigan; Anthony Botelho, Worcester Polytechnic Institute; Neil T Heffernan, Worcester Polytechnic Institute; Edward Wu, University of Michigan; Luke Miratrix, Harvard University
2:45 PM Machine Learning for Estimating Causal Effects from High-Dimensional Observational Data

Fredrik Johansson, MIT
3:05 PM Bayesian Inference for Sample Surveys in the Presence of High-Dimensional Auxiliary Information

Yutao Liu, Columbia University; Andrew Gelman, Columbia University; Qixuan Chen, Columbia University
3:25 PM Manipulation Proof Machine Learning
Daniel Bjorkegren, Brown University; Joshua Blumenstock, University of California Berkeley
3:45 PM Floor Discussion