Webinar to celebrate the new Joint Initiative for Causal Inference

Background:

Along with UC Berkeley and Novo Nordisk the Department of Public Health, and in particular the Section of Biostatistics, is proud to launch our new Joint Initiative for Causal Inference. The Joint Initiative will support an international effort to advance work at the intersection of statistical methods, machine learning, and causal inference methods. Causal inference is an area of scientific inquiry that uses formal mathematical frameworks to move from studying statistical associations to studying causal and effect relationships.

The joint initiative has two hubs: one at the University of Copenhagen and one at UC Berkeley. The UC Berkeley hub is hosted at the School of Public Health’s Center for Targeted Learning under the direction of Dr. Maya Petersen, the chair of Berkeley Public Health’s Division of Biostatistics, and Dr. Mark van der Laan, Jiann-Piang Hsu and Karl E. Peace Endowed Chair in Biostatistics. The Copenhagen hub is composed of Profs. Torben Martinussen and Thomas Gerds, Assoc. Prof. Theis Lange, Assistant Prof. Helene Rytgaard, post.doc Aksel Jensen and Phd student Marie Skov Breum.

You can read more about the initiative via this link 

Webinar:

Profs. Maya Petersen and Mark van der Laan has pioneered the fusing of causal thinking, machine learning and advanced statistics in their causal roadmap. This roadmap provides simultaneously a way to keep the causal question front and center (do not get side-tracked by technicalities) while at the same time using data optimally.

To celebrate the joint initiative and to increase awareness of the potentials we held a 3 * 2 hours webinar in late October.

All has been recorded and is available via this link (you have to register for the course to access the material, but this is free of charge)

The first day is targeted at any researcher with an interest in epidemiology/causal inference. No specific technical skills required.

Day two presents case studies while day three addresses issues at the forefront of current research. These two days require increasingly more technical background knowledge.