The workshop will take place on July 21th, 10:00 AM CEST on Zoom
Course Details
Most empirical researchers and decision makers are interested in causal questions. In this workshop, we will discuss what differentiates causal from other kind of questions and how we can understand assumptions that are needed to answer them. We will cover basic strategies based on observing control variables, issues surrounding the analysis of causal mechanism, as well as instrumental variable approaches. Throughout, we will use directed acyclic graphs (DAGs) to highlight crucial assumptions and to explain sources of bias, and we will discuss the use of various R packages for sensitivity analysis. Depending on participants' interests and if time permits, we can also discuss modern approaches to panel analysis or the causal foundations of missing data adjustments. Some (very) basic knowledge of R will be helpful.
Instructor
Dr. Julian Schuessler
Julian Schuessler is a post-doc at the Department of Political Science, Aarhus University, Denmark. He received his PhD from the University of Konstanz. His research focuses on causal inference and statistical problems, including instrumental variables, missing data, and power analysis for survey experiments, as well as public opinion on the European Union. He is a recipient of the American Statistical Association's "Causality in Statistics Education Award".
Schedule
Session Starts
Research Design and Causal Analysis with R (Part I)
Short Break
Session Continues
Research Design and Causal Analysis with R (Part II)
Session Ends
Content Licensing
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
