SDS 617: Causal Modeling and Sequence Data — with Sean Taylor

#CausalModeling #SequenceAnalytics #CausalExperimentation 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, https://datalore.online/SDS, the collaborative data science platform, and by Zencastr, http://zen.ai/sds, the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit https://www.jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • Sean on his new venture, Motif Analytics [2:44] • The relationship between causality and sequence analytics [13:37] • Sean's data science work at Lyft [20:40] • The key investments for large-scale causal experimentation [25:44] • Why and when is causal modeling helpful [30:51] • Causal modeling tools and recommendations [35:11] • Facebook's Prophet automation tool for forecasting [38:20] • What Sean looks for in data science hires [49:12] • Sean on his PhD in Information Systems [51:53] Additional materials: https://www.superdatascience.com/617