ADIA Lab and Crunch Lab Launch Causal Discovery Challenge

ADIA Lab and Crunch Lab have launched the second edition of their machine learning competition, the ADIA Lab Causal Discovery Challenge. The challenge focuses on advancing the field of Causal discovery, with a $100,000 prize pool for the best crowdsourced machine learning models.

One of the main challenges in developing truly intelligent machines is to enable them to understand cause and effect—going beyond mere correlations, which can often be deceptive and fail to capture the true nature of relationships between variables.

The ADIA Lab Causal Discovery Challenge will ask participants to uncover interactions between variables through visual representations called causal directed acyclic graphs (DAG). This approach has applications across diverse domains, including health sciences, economics, social sciences, environmental science, and education. Data scientists and machine learning researchers interested in participating can submit their algorithms directly on the Crunch Lab platform.

“After the success of our initial competition last year, which saw more than 5000 submissions, we are excited to continue our partnership with Crunch Lab,” said Dr. Horst Simon, Director of ADIA Lab. “This year we are focused on the crucial topic of Causal AI, which I’m sure will appeal to many of CrunchDAO’s 5,000 data scientists and 600 PhDs.

The challenge runs from July 25 to October 24, 2024. A total prize pool of $100,000 USD will be shared among the top ten entries, with the overall winner receiving $40,000 USD and the rest of the prize pool awarded to the remaining nine top participants.

Jean Herelle, CEO and co-founder of Crunch Lab, said: “For this second collaboration with ADIA Lab, we have the exciting opportunity to work with some of the brightest minds in the field of causality. This competition is a rallying cry for the data science community to move beyond mere correlations and focus on the true mechanisms behind observations. By understanding and discovering causal graphs, we can make better informed decisions, design more effective interventions, and drive scientific progress.

ADIA Lab Advisory Board members Prof. Guido Imbens, Prof. Miguel Hernan, and Prof. Marcos Lopez de Prado have been actively involved in the design of the ADIA Lab Causal Discovery Challenge and will remain engaged throughout the competition.