The Importance of Causal Modeling in Data Science

Dr. Sean Taylor, Co-Founder and Chief Scientist of Motif Analytics, joins Jon Krohn this week for yet another perspective on causal modeling. Tune in for a great conversation that covers large-scale causal experimentation, Information Systems, Bayesian parameter searches, and more.

This episode is brought to you by Datalore (datalore.online/SDS), the collaborative data science platform, and by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Sean on his new venture, Motif Analytics [4:23]
• The relationship between causality and sequence analytics [15:26]
• Sean's data science work at Lyft [22:21]
• The key investments for large-scale causal experimentation [27:25]
• Why and when is causal modeling helpful [32:34]
• Causal modeling tools and recommendations [36:52]
• Facebook's Prophet automation tool for forecasting [40:02]
• What Sean looks for in data science hires [50:57]
• Sean on his PhD in Information Systems [53:34]

Additional materials: www.superdatascience.com/617