“Overlap Weighting for Causal Inference,” presented by Dr. Fan Li, Duke University
Author: BD
Interdisciplinary Seminar: Susan Murphy, Harvard University
“Assessing Personalization in Digital Health,” presented by Dr. Susan Murphy, Harvard University
Paper of the Month: September 2021
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592., Cambridge, U.K., 1998.
Interdisciplinary Seminar: Jon Krosnick, Stanford University
“The Collapse of Scientific Standards in the World of High Visibility Survey Research,” presented by Dr. Jon Krosnick, Stanford University
Interdisciplinary Seminar: David Kaplan, University of Wisconsin-Madison
“Developments and Extensions in the Quantification of Model Uncertainty: A Bayesian Perspective,” presented by David Kaplan, University of Wisconsin-Madison
Interdisciplinary Seminar: Jennifer Hill, Columbia University
“thinkCausal: One Stop Shopping for Answering your Causal Inference Questions,” presented by Jennifer Hill, Columbia University
Interdisciplinary Seminar: Jean-Paul Fox, University of Twente
“Bayesian Covariance Structure Modeling: An Overview and New Developments,” presented by Jean-Paul Fox, University of Twente
Interdisciplinary Seminar: Susan Paddock, NORC at the University of Chicago
“Causal Inference Under Interference in Dynamic Therapy Group Studies,” presented by Dr. Susan Paddock, NORC University of Chicago
Paper of the Month: April 2021
Benjamini, Y. (2020). Selective Inference: The Silent Killer of Replicability. Harvard Data Science Review, 2(4).
Interdisciplinary Seminar: David Dunson, Duke University
“Bayesian Pyramids: Identifying Interpretable Deep Structure Underlying High-dimensional Data,” presented by Dr. David Dunson, Duke University