Workshop
4th MCML Workshop on Causal Machine Learning
Organized by Our PI Stefan Feuerriegel and His Team
12.08.2025
10:00 am - 8:00 pm
LMU Munich, Professor-Huber-Platz 2, W201
Causal inference has become highly important in many fields, such as medicine or marketing, and has received increasing attention from researchers in machine learning in recent years. The MCML Workshop in Causal Machine Learning invites causal ML researchers to present their work. The workshop aims to bring together researchers from different fields to discuss possible interconnections between their work and foster future collaborations.
Participation and Registration
Participation is open to all MCML members as well as researchers from other institutions. To register for the workshop, please fill in this Google Form by 31st July 2025.
Research Presentation
Participants are invited to give a talk on their research work in a 20-minute presentation (+ 10-minute Q&A). As presentation slots are limited, we kindly ask interested participants to apply for a presentation with a title and abstract of the work via email to both Yuchen Ma and Stefan Feuerriegel by 20th July 2025. Presentations will be selected based on scientific quality and diversity.
Preference will be given to works that are not published (i.e. working papers); please indicate so in your email.
Schedule
10:00 am – 10:15 am
Welcome
Stefan Feuerriegel, LMU
10:15 am – 11:15 am
Tutorial: DoubleML (https://docs.doubleml.org/stable/index.html)
Martin Spindler, Sven Klaassen, Economic AI
11:15 am – 11:45 am
Adjustment for Confounding using Pre-Trained Representations
Rickmer Schulte, LMU Munich
11:45 am – 1:15 pm
Lunch Break (lunch not provided)
Coffee will be served from 13:00 in the seminar room
1:15 pm – 2:15 pm
Keynote
Deep Learning and Causality: Bridging the Gap
Rahul G. Krishnan, University of Toronto
2:15 pm – 2:30 pm
Lab Presentation
Mathias Drton, TU Munich
2:30 pm – 3:00 pm
3:00 pm – 3:30 pm
Foundation Models for Causal Inference via Prior-Data Fitted Networks
Emil Javurek, LMU Munich
3:30 pm – 4:00 pm
Estimating Treatment Effects with Independent Component Analysis
Patrik Reizinger, Max Planck Institute
4:00 pm – 4:30 pm
4:30 pm – 5:00 pm
Causal Strategic Learning with Competitive Selection
Huynh Vo, Helmholtz-Zentrum
5:00 pm – 5:15 pm
Heterogeneous Treatment Effects in Survival Data: Application to Real-World-Data in the Context of Regulatory Decision-Making
Anna Weller, Fraunhofer (Short presentation)
5:15 pm – 5:30 pm
Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing
Oliver Schacht, University of Hamburg (Short presentation)
5:30 pm – 8:00 pm
Dinner & Networking 🥨
Beer garden; at own expenses
Notes: Presentation: DVI+VGA available. For Mac, please bring your converter.
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