12

Aug

Teaser image to 4th MCML Workshop on Causal Machine Learning

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: DobleML

Martin Spindler, Sven Klaassen, Economic AI


11:15 am – 11:45 am

Adjustment for Confounding using Pre-Trained Representations

Karl 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

Break

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

Break

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.

Organized by:

Stefan Feuerriegel and team MCML / Institute of AI in Management, LMU Munich


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