Workshop
5th MCML Workshop on Causal Machine Learning
Organized by Our PI Stefan Feuerriegel and His Team
13.08.2026
10:00 am - 8:00 pm
Professor-Huber-Platz 2, W201 (LMU Munich)
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 26th July 2026.
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 Yuxin Wang and Stefan Feuerriegel by 26th July 2026.
Preference will be given to works that are not published (i.e. working papers); please indicate so in your email.
Schedule
10:00 am – 10:10 am
Welcome
Stefan Feuerriegel, LMU
10:10 am – 11:10 am
Uncertainty quantification in Causal ML
Valentyn Melnychuk, LMU
11:10 am – 11:30 am
Causal Representation Learning for Climate Evolution under Diverse Forcing Scenarios
Shan Zhao, TUM
11:30 am – 1:30 pm
Lunch Break (lunch not provided)
Coffee will be served from 13:00 in the seminar room
1:30 pm – 2:30 pm
Causal Discovery in the Age of Weak Supervision
From Preference Data to Language Model Oracles
Sonali Parbhoo, Imperial College
2:30 pm – 3:00 pm
Coffee break
3:00 pm – 4:00 pm
Automatic Debiased Machine Learning for Static Sample Selection Models and Dynamic Treatment Regimes
Theresa M. A. Schmitz, Heinrich Heine University Düsseldorf
4:00 pm – 4:30 pm
Coffee Break
4:30 pm – 4:50 pm
Does a SARS-CoV-2 infection increase the risk of dementia? An application of causal time-to-event analysis to real-world patient data
Jannis Guski, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
4:50 pm – 5:10 pm
Bayesian ICA for Causal Discovery
Joe Suzuki, University of Osaka
5:10 pm – 5:30 pm
Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
Emil Javůrek, LMU
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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