Registration closed.
November 5–6 2024, online.
Supported by
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, 6.30PM CET, 9.30AM PDT.
Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. Aleksander is the founder and host of the Causal AI-centered Causal Bandits Podcast.
Ciarán Gilligan-Lee is the Head of the Causal Inference Research Lab at Spotify and an Honorary Associate Professor at University College London. His research focuses on causal inference and its applications, with notable contributions in the fields of healthcare, privacy, and decision-making.
His work has been described as a "breakthrough" by Newsweek, and MIT Technology Review noted that it "could supercharge medical AI" and "is set to improve automated decision making in finance, healthcare, ad targeting, and more."
The live podcast will be streamed on November 6, 6.30PM CET, 9.30AM PDT (Zoom, register above).
Explore cutting-edge methodologies and engage in discussions about the role and impact of causality in machine learning.
Bridge the gap between theory and practice by exploring real-world applications of causal data science.
Understand the role of causality for fairness, robustness, and discrimination in policy-making.
Image: ID 241391100 © Evgeny Turaev | Dreamstime.com
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
XPlain Data
Xplain Data’s Causal Discovery algorithms enable companies in all industries to identify crucial hidden causal relationships in their real world data. By knowing why something is happening, it is possible to target and potentially eliminate causes of errors or achieve a desired effect. Leading enterprises in mechanical engineering, manufacturing, and healthcare use Xplain Data for advanced analyses, production and yield optimization, and care management.
Open Positions
Senior Data Scientist
