How causal machine learning can improve business decisions – Jermain Kaminski

Contextual knowledge is integral to understanding why we behave the way we do. Why we might buy one product over another, invest in that crowdfunding campaign or choose to watch a particular movie, these decisions are governed by a complex set of functional, social and emotional reasons. Whilst this may be something humans take for granted, machines need to be taught how to do this. According to the economist Clayton Christensen, the causality behind why someone might purchase a particular solution is what matters most in innovation, not correlation.

In this video, Jermain Kaminski, an assistant professor at Maastricht University, talks about his recent research on experimentation and causal machine learning in entrepreneurship and innovation. He explains how applying causal models to big data can help businesses and entrepreneurs make better business decisions in positioning and improving their products.
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Video produced by https://www.concreet.media/
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Interested in the topic?

- Hopp, C., Antons, D., Kaminski, J., & Salge, T. O. (2018). What 40 Years of Research Reveals About the Difference Between Disruptive and Radical Innovation. Harvard Business Review.

- Hopp, C., Antons, D., Kaminski, J., & Salge, T. O. (2018). Disruptive Innovation: Conceptual Foundations, Empirical Evidence, and Research Opportunities in the Digital Age. Journal of Product Innovation Management, 35(3), 446-457.

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This video is part of our series ‘Talking Business and Economics’ in which experts share their insights and knowledge on different topics in the field of business and economics.

Check out the rest of the series: https://www.youtube.com/playlist?list=PL3O9AsHHdTrESUObvWodFg2BmeQ6XCgeW
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Видео How causal machine learning can improve business decisions – Jermain Kaminski канала School of Business and Economics