Jakob Runge: Causal Inference on Time Series Data with the Tigramite Package скачать в хорошем качестве

Jakob Runge: Causal Inference on Time Series Data with the Tigramite Package

Subscribe to the channel to get notified when we release a new video. Like the video to tell YouTube that you want more content like this on your feed. See our website for future seminars: https://sites.google.com/view/ocis/home Tuesday, Oct 14, 2025: Jakob Runge (University of Potsdam) Title: Causal Inference on Time Series Data with the Tigramite Package Abstract: This talk introduces the open-source Python package Tigramite, which implements constraint-based algorithms such as PCMCI+ and many variants thereof as methods optimised for causal discovery on time series. In addition, Tigramite features causal effect estimation using optimal adjustment. I will outline the basic ideas behind PCMCI and optimal adjustment and then demonstrate practical workflows in Tigramite, including a user-friendly guide to choosing methods in causal inference based on causal questions, assumptions and available data. I look forward to feedback and exchange on improvements of the package to make causal inference accessible for practitioners dealing with time series data.