Jacobs / Cifar Workshop
Causal Inference from Longitudinal Data
March 29th · March 30th · April 1st
(Three 3h-Sessions, Online, No Workshop on March 31st)
experts in longitudinal data analysis present cutting edge methods in their respective fields and interactively discuss how these methods can be applied to other fields.
Neuroscience · Psychology · Philosophy · Political Science · Epidemiology
People are often interested in making a causal inference from time-series or longitudinal data - data in which one or many individuals are assessed at more than one time point (such as panel data, intensive longitudinal data, multi-trial experimental data and neuroimaging data). Over the past decades, there has been considerable progress in the methods to infer causality from longitudinal data, but they are developed in respective fields in relative isolation, and with limited cross-pollination of ideas across disciplines. In this workshop, experts in various fields (e.g., neuroscience, political science, psychology, epidemiology, philosophy) present methods and ideas on longitudinal data analysis and causal inference. Through the presentations and panel discussions, the workshop aims to interactively highlight the strengths and limitations of causal inference methods on longitudinal data from a highly cross-disciplinary perspective. The workshop is targeted for applied researchers in any fields working on any type of longitudinal or time-series data with the motivation to learn the topic from an interdisciplinary perspective.