Bridging Industry and Academia
in Causal Data Science

Fostering a dialogue between industry and academia on causal data science.

Save the date
November 5–6 2024, online.

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Call for Papers01

The Causal Data Science Meeting 2024 aims to foster an interdisciplinary dialogue between data scientists from industry and academia on the role of causality in machine learning and AI

Causality has long been an important topic in various disciplines including computer science, economics, the social sciences, epidemiology, and philosophy. In recent years, interest has also grown in the business sector with both experimental (such as A/B testing, reinforcement learning, business experimentation) and observational causal inference methods (such causal discovery, root-cause analysis, quasi-experimental methods) being increasingly applied by practitioners.

Ever since its launch, the Causal Data Science Meeting has been at the forefront of this development, connecting a global audience of causality experts from academia and industry. We are excited to announce this year's Causal Data Science Meeting, which will take place as a two-day virtual conference on November 5-6, 2024. The event will focus on the newest methodological advances, practical aspects, and organizational challenges related to the adoption of causal machine learning tools. It will feature invited talks and presentations of accepted proposals.

Workshop Date:
November 5–6
Submission Deadline:
September 29
Acceptance Notification:
October 6

Please submit your presentation proposal, extended abstract or full paper to

The meeting is organized as a workshop for the purpose of facilitating discussion and disseminating ideas. No conference proceedings of accepted presentations will be published. If you want to register as a participant, without presenting, please click

If your organization would like to sponsor the event, please contact us at

  • Advances in causal machine learning and artificial intelligence
  • Applications of novel causal inference methods in research and to business-relevant problems
  • Experimentation & A/B testing
  • Causal discovery and root-cause analysis
  • Interplay between causality and generative AI
  • Causal inference methods in statistics and econometrics
  • Organizational challenges and best practice examples for the implementation of causal inference in industry
  • Interplay between causality Insights from practice on challenges and opportunities of causal data science
  • (Open-source) software for causal inference
  • Causal ML/AI for business decision-making

"Causal inference in economics is fundamentally about determining the impact of one variable on another, a core task in policy evaluation."

Dominik Janzing

Professor Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford, and Harvard. She is an elected member of the National Academy of Science and is the recipient of the John Bates Clark Medal, awarded by the American Economics Association to the economist under 40 who has made the greatest contributions to thought and knowledge.

Her research is in the areas of the economics of digitization, marketplace design, and the intersection of machine learning and econometrics. She has studied a range of application areas, including timber auctions, online advertising, the news media, and the application of technology for social impact.

We are very happy to welcome Professor Athey to her keynote on November 5.

Who it's for03

Research meets practice.

Students and Researchers

Explore cutting-edge methodologies and engage in discussions about the role and impact of causality in machine learning.

Industry Professionals

Bridge the gap between theory and practice by exploring real-world applications of causal data science.

Policy Makers

Understand the role of causality for fairness, robustness, and discrimination in policy-making.

Image: ID 241391100 © Evgeny Turaev |

About the Meeting04

Providing an open space to advance the frontier of causal data science.

The Causal Data Science Meeting was founded in 2020 and thought as as a small-scale workshop for 50 attendees. However, already in its first year, CDSM received an overwhelming response of 900 pre-registrations, which encouraged us to continue the event annually. We aim to create a friendly, efficient, and constructive environment for academics and practitioners to exchange ideas on all causality-related topics in data science and machine learning. We strive to maintain transparency in our non-profit goal and use all sponsorships received to cover smaller expenses and PhD research.


Participants since 2020


Accepted presentations

Past keynote speakers

Judea Pearl
Silivia Chiappa
Google DeepMind
Guido Imbens
Stanford University
Sara Magliacane
Amsterdam Machine
Learning Lab
Elias Bareinboim
Columbia University
Sean Taylor
Dominik Janzing
Amazon Research

What participants say

I am thrilled to see people from different disciplines come together.

Guido Imbens
Stanford University, Recipient of the 2021 Nobel Memorial Prize in Economic Sciences

The next revolution will be even more impactful upon realizing that data science is the science of interpreting reality, not of summarizing data.

Judea Pearl
UCLA, Recipient of the Turing Award in 2011, Author of The Book of Why

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The Causal Data Science Meeting 2024 is jointly organized by researchers from Maastricht University, Netherlands, and Copenhagen Business School, Denmark.

Paul Hünermund
Assistant Professor, Copenhagen Business School
Jermain Kaminski
Assistant Professor, Maastricht University
Carla Schmitt
PhD Candidate, Maastricht University
Beyers Louw
PhD Candidate, Maastricht University
Maastricht University Tapijn