Keyword Search
Sessions Were Renumbered as of May 19.
Legend:
CC-W = McCormick Place Convention Center, West Building, CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago, UC= Conference Chicago at University Center
* = applied session ! = JSM meeting theme
|
Keyword Search Criteria: observational returned 31 record(s)
|
|
Sunday, 07/31/2016
|
Estimating the Causal Impact of Recommendation Systems from Observational Data
Amit Sharma, Microsoft Research; Jake Hofman, Microsoft Research; Duncan Watts, Microsoft Research
|
Causal Inference from Observational Studies with Partial Interference
Brian Barkley, The University of North Carolina at Chapel Hill; Michael Hudgens, The University of North Carolina at Chapel Hill
2:05 PM
|
Causal Inferences from Observational Studies: Fracking, Earthquakes, and Oklahoma
Howard Wainer, NBME
4:05 PM
|
Identification of Marginal Causal Relationships in Gene Networks from Observational and Interventional Expression Data
Andrea Rau, INRA; Gilles Monneret, INRA/Universite Pierre et Marie Curie; Florence Jaffrezic, INRA; Gregory Nuel, Universite Pierre et Marie Curie
4:05 PM
|
Maximizing the Information Content of a Balanced Matched Sample
Jose Zubizarreta, Columbia University; Cinar Kilcioglu, Columbia University
4:05 PM
|
Constructed Second Control Groups and Attenuation of Unmeasured Biases
Samuel D. Pimentel, University of Pennsylvania; Dylan Small, University of Pennsylvania; Paul R. Rosenbaum, University of Pennsylvania
4:45 PM
|
Propensity Score Calipers and the Overlap Condition
Ben Hansen, University of Michigan
5:05 PM
|
|
Tuesday, 08/02/2016
|
Bayesian Propensity Score Analysis for Observational Multilevel Studies
Qi Zhou; Joon Jin Song, Baylor University; Catherine J. McNeal, Baylor Scott & White Health; Laurel A. Copeland, Baylor Scott & White Health; Justin Philip Zachariah, Texas Children's Hospital/Baylor College of Medicine
|
Bayesian Causal Inference Analyses with Unmeasured Confounders
Negar Jaberansari, University of Cincinnati; Bin Huang, Cincinnati Children's Hospital Medical Center
|
Search for Truth Amidst the Bias: Evaluate the Impact of Unmeasured Confounding in Comparative Observational Studies
Xiang Zhang, Eli Lilly and Company; Douglas Faries, Eli Lilly and Company
|
Matching Estimators for Causal Effects with Multiple Treatments
Anthony Scotina; Roee Gutman, Brown University
|
Matching Estimators for Causal Effects with Multiple Treatments
Anthony Scotina; Roee Gutman, Brown University
8:45 AM
|
Causal Inference in Network-Dependent Observational Data
Oleg Sofrygin, University of California at Berkeley; Mark van der Laan, University of California at Berkeley
8:55 AM
|
Assessing Time-Varying Causal Effect Moderation in Mobile Health
Daniel Almirall, University of Michigan Survey Research Center; Audrey Boruvka, University of Michigan; Katie Witkiewitz, University of New Mexico; Susan A. Murphy, University of Michigan
11:00 AM
|
Design of Sample Surveys That Complement Observational Data to Achieve Population Coverage
Eric Slud, U.S. Census Bureau; Robert Ashmead, U.S. Census Bureau
11:20 AM
|
Instrumental Variable Estimation in Observational Studies
Miguel Hernan, Harvard
2:55 PM
|
|
Wednesday, 08/03/2016
|
Practical Guidance and Tools for Rule-Out Sensitivity to Unmeasured Confounding Analyses
Lucy D'Agostino McGowan; Robert Greevy, Vanderbilt University
|
Using Propensity Scores to Infer Causal Effects on Heart Health from Chemotherapy Treatment of Breast Cancer Patients
John Craycroft, University of Louisville; Maiying Kong, University of Louisville; Carrie Lenneman, University of Louisville
8:35 AM
|
False Discovery Rate Control for Effect Modification in Observational Studies
Bikram Karmakar, University of Pennsylvania; Ruth Heller, Tel-Aviv University; Dylan Small, University of Pennsylvania
9:05 AM
|
Proximity Score Matching: Using the Random Forest Proximity Matrix for Matching in Causal Inference
Hui Fen Tan, Cornell University; David Isaac Miller, Northwestern University; James Savage, Lendable
10:35 AM
|
Discovering Effect Modification in Matched Observational Studies with Multiple Controls
Kwonsang Lee, University of Pennsylvania; Dylan Small, University of Pennsylvania; Paul R. Rosenbaum, University of Pennsylvania
10:50 AM
|
Leveraging Multiple Outcomes in Matched Observational Studies
Colin B. Fogarty, MIT; Dylan Small, University of Pennsylvania
11:05 AM
|
Bagged One-to-One Matching for Efficient and Robust Treatment Effect Estimation
Lauren Samuels, Vanderbilt University; Robert Greevy, Vanderbilt University
11:50 AM
|
Propensity Score Matching Using Random Forest in Educational Data Mining Problems
Richard Levine, San Diego State University
2:05 PM
|
|
Thursday, 08/04/2016
|
Build Individualized Treatment Rule on Scale Using Health Care Data with Genomic Information
Jin Zhou, University of Arizona; Haoda Fu; Kevin Doubleday, University of Arizona
10:35 AM
|
How to Control for Unmeasured Confounding in an Observational Time-to-Event Study with Exposure Incidence Information: The Treatment Choice Cox Model
James Troendle, National Institutes of Health; Zhiwei Zhang, FDA/CDRH; Eric Leifer, National Heart, Lung, and Blood Institute; Song Yang, National Heart, Lung, and Blood Institute; Michael Sklar, University of Pennsylvania; Heather Jerry, Nebraska Department of Health and Human Services
11:35 AM
|
Causal Inference in Observational Studies using data integration in large scales and creating strong instrumental variables
Azam Yazdani; Akram Yazdani, The University of Texas Health Science Center at Houston; Ahmad Samiei, Hasso-Plattner-Institut für Softwaresystemtechnik; Eric Boerwinkle, The University of Texas Health Science Center at Houston
11:35 AM
|
Quantification of Imbalances in Baseline Covariates in Observational Studies
Adin-Cristian Andrei, Northwestern University
11:50 AM
|
On Propensity Score Weighting Approaches to Observational Studies with Survival Outcome
Huzhang Mao; Liang Li, MD Anderson Cancer Center
11:50 AM
|
|
|
