Causality in Clinical Research: What, Why, When & How - day two

Program Overview & Target Audience In just two half-days on December 3 & 4, starting at 9 a.m. and ending at 1 p.m ET., top experts from Penn, Rutgers, Johns Hopkins, and Vanderbilt will teach a variety of fundamental topics in causal inference including propensity score matching and weighting, instrumental variables, difference-in-differences study designs, and machine learning approaches. All methods will be illustrated using real clinical examples. The course will be specifically aimed towards clinical researchers who have a basic understanding of statistics, but who perhaps have not had formal training in causal inference concepts and methods.Learning Objectives Upon completing this activity, learners should be able to: Review the history of causality in the context of clinical research Explain the difference between association versus causation Describe various observational study designs Apply analytical strategies for measured and unmeasured confounding Virtual short course: December 3-4, 2020 AgendaDecember 3: 9:00-9:10 Opening Remarks and Introductions Nandita Mitra, PhD Professor of Biostatistics University of Pennsylvania 9:10-9:50 A Brief History of Causality Elizabeth Ogburn, PhD Associate Professor of Biostatistics Johns Hopkins University 10-11:20 An introduction to causal concepts: Potential outcomes, DAGs, confounding Alisa Stephens-Shields, PhD Assistant Professor of Biostatistics University of Pennsylvania 11:30-1:00 Propensity scores: matching, weighting Andrew Spieker, PhD Assistant Professor of Biostatistics Vanderbilt University December 4: 9:00-10:20 Instrumental Variables and Unmeasured Confounding Nandita Mitra, PhD Professor of Biostatistics University of Pennsylvania 10:30-11:30 Causal Study Designs: Difference-in-Differences and Regression Discontinuity Luke Keele, PhD Associate Professor of Applied Statistics University of Pennsylvania 11:40-1:00 Machine Learning Approaches to Causal Inference Jason Roy, PhD Professor and Chair of Biostatistics Rutgers University