The CAUSALTIME project develops advanced statistical methods that allow the study of complex causal relationships between multiple time-varying phenomena in various fields of science and society at large. The project enables this by combining theoretical causal identification research, computational Bayesian inference methods, and novel visualisation techniques for the creation of freely available open-source software for efficient, interpretable, and transparent causal effect estimation, visualization, dissemination, and decision-making based on temporal data.

2023

2027