Focus
In this workshop, we will examine the main obstacles to increasing the real-world impact of causal data science—including causal machine learning, causal representation learning, causal discovery, and causal inference. Although these areas have produced many novel methods and theoretical advances, their adoption in empirical sciences remains limited, which in turn constrains their societal impact.
Using examples from health, social, and earth sciences, we will highlight practical barriers to applying causal methods. Through these domain-specific lenses, we will identify key open problems and outline concrete pathways to broaden the use of causal approaches in real‑world settings.
Keynote Speakers
Participation
This workshop has the goal of fostering meaningful dialogue between causal methods developers and practitioners across application domains. To maximize this cross-pollination, we encourage participants to submit an abstract and actively participate in the poster session and in the structured breakout discussion sessions that follow each keynote presentation.
All participants must register for the main EurIPS 2025 conference . We may be able to offer travel and accommodation grants to support diverse participants, prioritizing self-identified minorities and individuals from the Global South—we will update this page as soon as possible with more information.
We are dedicated to creating a safe and inclusive environment for all participants and do not tolerate harassment of participants in any form, adhering to the QueerInAI code of conduct . If you experience or witness an issue, or have concerns about inclusiveness or accessibility, please contact one of the organizers in person or via email.