Workshop “From Data To Causes”
The project “From Data to Causes – Advancing Research and Education on the Missing Link of Causal Inference” is funded through the Melbourne/Berlin Research Partnership Program of the Berlin University Alliance and the University of Melbourne. The project started with a kick-off session in spring 2021 and comprised an international, multidisciplinary two-day online workshop in Fall 2021.
- Causal Inference Using Parametric Structural Equation Models by Christian Gische
- From Data to Causes: Building a General Cross-Lagged Panel Model by Michael Zyphur
- Reconsidering “Standard” Approaches to Causal Inference in Continuous Time by Manuel Voelkle
- Which Longitudinal Model Should I Choose by Kenneth Bollen, University of North Carolina at Chapel Hill
- From Data to Causes: Perspectives on Causation from Psychology, Physics, and Dynamical Systems by Pascal Deboeck, University of Utah
- Less Casual Causal Inference by Julia Rohrer, Universität Leipzig