Practical aspects of causal inference

 

Course tools

  • Python/R notebooks

Prerequisites

  • Basic knowledge of probability theory and regression, and programming in Python/R.

Level of complexity of course

Intermediate

Lecturer

Mr. Sebastian Weichwald
The University of Copenhagen.

Sebastian is an advocate of pragmatic causal modeling, aims at bringing statistical causal modeling from pen and paper to fruitful application, and recently won the Causality 4 Climate NeurIPS competition 2019 together with his team. He pursues conceptual work on how our ability to causally reason about a system depends on the variables and transformations thereof being used as descriptors (check out their 2017 UAI paper on this). Sebastian is a Postdoc at the CoCaLa (Copenhagen Causality Lab), Department of Mathematical Sciences, University of Copenhagen. He obtained his Ph.D. from the Max Planck Institute for Intelligent Systems and the ETH Zurich, his MSc in Computational Statistics and Machine Learning from University College London, his BSc in Mathematics from the University of Tübingen, and was funded by the German National Academic Foundation (Studienstiftung) and the German Academic Exchange Service (DAAD).

Fields of interests: Causal Modelling, Machine Learning

Contacts: https://sweichwald.de

Mr. Sorawit Saengkyongam
The University of Copenhagen.

Sorawit is a Ph.D. student in Statistics at Copenhagen Causality Lab, University of Copenhagen, where he is co-supervised by Jonas Peters and Niklas Pfister. I completed my master’s degree in Machine Learning at University College London in 2019, during which Sorawit worked with Ricardo Silva. Prior to the master’s study, Sorawit worked as a data scientist at Agoda for four years. He received my bachelor’s degree in Statistics from Chulalongkorn University in 2014.

Fields of interests: Causality, Reinforcement Learning

Contacts:  https://sorawitj.github.io