About the Course
This workshop aims to give a broad introduction to causal inference as an “old” problem now being enriched by machine learning.
We will cover a lot of ground, from Causality as a framework to learning causal effects from observational data, experiments, and causal structure learning. We will provide hands-on exercises with e-commerce data, and will discuss modern ideas such as Orthogonal ML and heterogeneous treatment effects.
The goal of this day is to appreciate questions of causality in data science, to recognize them when you see them, to gain hands-on experience, and to provide participants knowledge to easily navigate existing literature. To foster this, we encourage participants to bring their data scientific problems along for a "Bring your data" session.
Finally, we will provide a brief glimpse into ongoing research on cause-effect estimation from observational data.
Venue
The workshop will take place on May 6, 2021, from 10am to 5pm. The event will be purely virtual using a zoom remote meeting. The event details will be provided to all registered participants before the course.
Speakers
We are delighted to welcome Lars Roemheld as our invited speaker and main instructor of this course.
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Lars Roemheld currently serves as Director of AI & Data at the German Ministry of Health’s digitalization task force. He previously held senior positions at the AI specialist QuantCo, where he used machine learning to develop anti-fraud and pricing solutions for financial, retail, and healthcare organizations in the US and Europe. A Data Scientist, Economist and Philosopher, he holds degrees from Stanford University and University of Heidelberg. Lars Roemheld is an expert in Causal Inference, Machine Learning, and, increasingly, the wondrous workings of government. |
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Niki Kilbertus will assist Lars during the course and provide a glimpse into ongoing research on causal inference. Niki is a group leader at HelmholtzAI in Munich and a TUM Junior Fellow working on causality as well as socially beneficial machine learning (fairness, privacy, performativity, ...). Niki obtained his PhD in machine learning as a Cambridge-Tübingen fellow from the University of Cambridge. |
This event is co-organised by Peter Steinbach (Helmholtz AI Consultant Team Lead, HZDR) and Niki Kilbertus (HMGU).
