617: Causal Modeling and Sequence Data

<p>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.</p><p>This episode is brought to you by Datalore (<a href='https://datalore.online/SDS'>datalore.online/SDS</a>), the collaborative data science platform, and by Zencastr (<a href='http://zen.ai/sds'>zen.ai/sds</a>), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit <a href='https://www.jonkrohn.com/podcast'>JonKrohn.com/podcast</a> for sponsorship information.<br/><br/></p><p>In this episode you will learn:<br/>• Sean on his new venture, Motif Analytics [4:23]<br/>• The relationship between causality and sequence analytics [15:26]<br/>• Sean&apos;s data science work at Lyft [22:21]<br/>• The key investments for large-scale causal experimentation [27:25]<br/>• Why and when is causal modeling helpful [32:34]<br/>• Causal modeling tools and recommendations [36:52]<br/>• Facebook&apos;s Prophet automation tool for forecasting [40:02]<br/>• What Sean looks for in data science hires [50:57]<br/>• Sean on his PhD in Information Systems [53:34]<br/><br/></p><p>Additional materials: <a href='https://www.superdatascience.com/617'>www.superdatascience.com/617</a></p>