Causal event process models, local independence and nonparametric inference by Niels Richard Hansen and Lasse Petersen, Dept. of Mathematical Sciences, University of Copenhagen.
You are all cordially invited to our upcoming HyFlexMultiverse seminar
Tuesday June 15th 16:00 (please note time!)
Abstract: In the first part of the talk, we will give a review of how causal models of event processes and local independence graphs are linked. In particular, how local independence graphs can represent partially observed systems and how local independence testing can be used to infer local independence graphs.
Abstract: In the first part of the talk, we will give a review of how causal models of event processes and local independence graphs are linked. In particular, how local independence graphs can represent partially observed systems and how local independence testing can be used to infer local independence graphs.
In the second part of the talk, we will show how to build flexible models of event intensities using a deep learning framework, specifically Tensorflow. Combined with double machine learning techniques, this makes nonparametric local independence testing feasible. However, the Tensorflow implementation may be of independent interest for other nonparametric modeling purposes.
Room: Hybrid meeting between physical attendance at room 5.2.46 (Biostats library) and Zoom: https://ucph-ku.zoom.us/j/67906140839?pwd=NEFZcm9oaVpZMDdVZVovOGNHNHkxdz09
IF POSSIBLE then please participate online to keep the physical turnout low in these times.
For a detailed overview of planned future seminars at the section of Biostatistics, UCPH, see http://biostatistics.dk/seminars/
IF POSSIBLE then please participate online to keep the physical turnout low in these times.
For a detailed overview of planned future seminars at the section of Biostatistics, UCPH, see http://biostatistics.dk/seminars/