Discovering Instantaneous Granger Causalities in Non-stationary Categorical Time Series Data. Application to auditory category learning by trial and error.

The meetings of the Causal Inference Working Group serve as a platform for collaboration and discussion, focussing on causal data analysis in academia and industry.

Using causal techniques, the group aims to develop intelligent strategies for data analysis that surpass the correlation based approach of classical statistics, in order to distill robust causal information from the data.

They meet every three weeks for a virtual session of one hour, according to their meeting schedule. The meetings feature talks from group members, discussions about current challenges in daily working life and the sharing of useful causality resources. The project has just started and the working group is still looking for participants who are curious about causal data science! Previous knowledge of causal inference is not necessary, but it might be helpful to be familiar with some basics of statistics, data science and machine learning.

Tuesday, 06-21-2022, 03.00 p.m. – 04.00 p.m., virtual event

More details at: https://gitlab.com/causal-inference/working-group/-/wikis/Meeting-schedule.

To participate in the event, please send an email to causal-inference@posteo.de.

This is an event of the Causal Inference Working Group.