#OPENBOX - Navigating Causal Discovery with Aleksander Molak

OPENBOX aims at bringing an easier understanding of open problems that helps in finding solutions for such problems. For the said purpose, I interview researchers and practitioners who have published works on open problems in a variety of areas of Artificial Intelligence and Machine Learning to collect a simplified understanding of these open problems. These are published as podcast series. This project is done in collaboration with ForHumanity. ForHumanity is a 501(c)(3) nonprofit organization dedicated to minimizing the downside risks of AI and autonomous systems. ForHumanity develops criteria for an independent audit of AI systems. To know more visit https://forhumanity.center/.  Today, we have with us Aleksandr. Aleksander Molakis a Machine Learning Researcher, Educator, Consultant,and Authorwho 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. He is the author of the book Causal inference and discovery in Python. This is Part 1. He discusses about open issues and considerations in causal discovery, Directed acrylic graphs, and Causal effect estimators. --- Send in a voice message: https://podcasters.spotify.com/pod/show/ryan-carrier3/message