Stat4Reg is a research laboratory at Umeå University, where scientists work at developing Causal Machine Learning models, methods and free software for the analysis of large individual databases, including linkages of health, socio-economics, demographic and other administrative registers (record linkage data), longitudinal studies (brain imaging, intervention programs, panel studies, ...), etc.
Stat4Reg at Github: github.com/stat4reg
Three projects have been awarded funding by the Swedish Research Council this Fall: "Dynamic relations between cardiovascular, social and behaviourial risk factors of cognitive ageing"; "Innovative machine learning methods for comparison of predictions and outcomes in register data"; "Robots with Causal Capabilities".
Maria Josefsson is invited speaker at the International Biometric Conference in Riga, 10-15 July, in the session "Recent developments in probabilistic machine learning methods for causal inference"; and Xavier de Luna is invited speaker at the meeting Challenges for Categorical Data Analysis in Perugia, 12-13 May.
Marie Eriksson is moderator for the session "Potentials for AI in National Quality Registers" at the "Quality Registers for research Day" in Stockholm, 5 May.
The project "Machine learning to study causality with big datasets: towards methods yielding valid statistical conclusions" has been awarded funding from the Wallenberg Foundation and from the Swedish Research Council.
Open reseach assistant position (5 months): announcement (in Swedish)
Stat4Reg researchers are organizing three invited sessions at the Virtual CMStatistics, December 2020.
New PhD student position (causal mediation analysis)
Stat4Reg members organized two invited sessions and gave four talks at the CMStatistics meeting in London, December 2019; on topics related to machine learning for causal inference and missing data problems. See program.
Job: We are looking for a programmer (C++, Python, parallel programming, etc) to work with our researchers at implementing novel machine learning methods
PhD defence: Methods for longitudinal brain imaging studies with dropout - Feb 2019
Conference on Machine Learning — Registration deadline 15 January
PhD defence: Methods for improving covariate balance in observational studies