Causal Modeling and Sequence Data | SDS 617

<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://gate.sc/?url=https%3A%2F%2Fdatalore.online%2FSDS&amp;token=f79067-1-1665477137412'>datalore.online/SDS</a>), the collaborative data science platform, and by Zencastr (<a href='https://gate.sc/?url=http%3A%2F%2Fzen.ai%2Fsds&amp;token=1e947b-1-1665477137412'>zen.ai/sds</a>), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Email <a href='mailto:natalie@jonkrohn.com'>natalie@jonkrohn.com</a> for sponsorship information.</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]</p><p>Additional materials: <a href='https://gate.sc/?url=http%3A%2F%2Fwww.superdatascience.com%2F617&amp;token=e07989-1-1665477137412'>www.superdatascience.com/617</a></p>