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Thank you, everyone!

We are delighted to share a summary of the Causal Data Science Meeting 2020 with you. This year's event was jointly organized by Maastricht University and Copenhagen Business School.


Over 900 participants from academia and industry

Correlation does not imply causation' is a famous phrase, not only known within the academic ivory tower, but also increasingly noticed in the data science departments of large corporations.

On 11–12th November, the Maastricht University School of Business and Economics and Copenhagen Business School hosted the Causal Data Science Meeting 2020. The conference was organized by Jermain Kaminski, Carla Schmitt and Beyers Louw from the Organisation, Strategy and Entrepreneurship department at Maastricht University’s School of Business and Economics, together with Paul Hünermund from the Department of Strategy and Innovation at Copenhagen Business School. This two-day virtual conference brought together experts from academia and industry who work on one of the most fundamental questions in data science – discerning cause and effect from mere correlation. The event had 950 registered participants and included 26 speakers from academic institutions such as Harvard, MIT, Stanford, Chicago and UCLA, as well as from industry labs at Microsoft Research, Total, Zalando, and the Google Chief Economics Team. In addition to the speakers, the event welcomed students and further external attendees from corporations around the world, including companies such as Amazon, Apple,, DeepMind, ING, Netflix, Novartis, and Spotify, as well as several smaller startups in the field of machine learning. The list of attendees featured many software and technology companies, as their products and services highly depend on experimentation for better customer experiences and business outcomes.

The conference showed the great potential of virtual conferencing. Given that it was the first time we organized this event, we didn’t expect such a great line-up of speakers and large number of attendees. But this tells us that there’s definitely high demand for causal data science in the business field.

– Paul Hünermund

Keynote presentations

Carla Schmitt, PhD student at the Department for Organization, Strategy and Entrepreneurship, introduced the first keynote speaker, Sean Taylor, with a quote from famous HBR innovation scholar Clayton Christensen: “Correlation does not reveal the one thing that matters most in innovation – the causality behind why I might purchase a particular solution.” Sean, who is head of the Rideshare Labs at Lyft, presented the causal inferences challenges that are central to Lyft’s business model, emphasizing the importance of causal inference in the ridesharing industry and how economic incentives can improve both drivers’ and passengers’ experiences.

Elias Bareinboim, associate professor in the Department of Computer Science and director of the Causal Artificial Intelligence Lab at Columbia University (, gave the second keynote address of the event. Elias presented an overview of the latest advances in causal data science and how they can help to solve long-standing challenges in empirical research, including confounding bias, selection bias, measurement error and the generalizability of experimental studies. His presentation highlighted that most statistical analyses are still not able to go beyond comparing apples with oranges, if the data generation process is not correctly taken into account.

Connecting science and industry

Causal inference is a pressing and yet underserved topic in many corporation’s strategy departments and data science centers. We started to notice a growing interest from practitioners, which is why we wanted to create a platform on which academia and industry can better connect and exchange ideas.

– Jermain Kaminski

One central theme became clear: There is huge value to be gained from effective industry-science collaboration and establishing a productive interdisciplinary dialogue around data science and causal inference, which renders the Causal Data Science Meeting (hopefully) all the more valuable.

This is exactly why I decided to pursue a PhD, to help firms ask better questions and bridge the gap between academia and industry. Causal inference is one of the most fundamental aspects of research and the starting point for higher reasoning. This conference provided a platform to enable exactly that and I am looking forward to similar events in the future.

– Beyers Louw

The organizing committee would like to thank Maastricht University and Copenhagen Business School for funding and support as well as the speakers and attendees for turning the first installment of the Causal Data Science Meeting into such a great success. Contact the organizing team if you have any questions or visit the website for more information:

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