6.2 David Kil, Data analytics, causal AI, and its application to university experiential education understating of outcomes and potential ranking

Topics discussed in this Episode include:

David’s origin story as an electrical engineering major (who liked chemistry) but also got interested in people and took an MBA degree at night. He worked in the defense industry in signal processing and then at Humanna analyzing medical issues with predictive outcome analytics. This led to an emphasis on human interactions and even empathy from the medical providers in successfully helping patients on their health care recovery journey.David met Jim first at CUNY and then at SUNY where they worked together on tying together the salary data from the New York State Department of Labor with the enrollment and major data from several SUNY universities about 10 years ago, particularly focusing on the presence of an internship in that program in the university.Discussion of data analytics, AI, and the need for causal inferences from AI and not just outcome measures to improve student graduation success and career success after graduation, possibly involving experiential education.Discussion of causal insights from data analytics, particularly AI and its application to helping students and families choose the right higher education institution for them taking into account a wide range of variables. Ending discussion of how the world is changing and how causal AI can partner with humans to help with decisions (like college selection) that lead to outcomes.

Resources discussed in or related to this episode:

https://cmlinsight.com/https://www.usnews.com/best-colleges/rankings/national-universitieshttps://en.wikipedia.org/wiki/Experiential_education