CAUSALTIME: Bayesian causal inference for multivariate longitudinal data
Description of the granted funding
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.
Show moreStarting year
2023
End year
2027
Granted funding
Funder
Research Council of Finland
Funding instrument
Academy research fellows
Other information
Funding decision number
355153
Fields of science
Statistics and probability
Research fields
Tilastotiede
Identified topics
computer science, information science, algorithms