Video title: Causal Models in Practice at Lyft with Sean Taylor - #486

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Causal Models in Practice at Lyft with Sean Taylor - #486
 

Description:

Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. 

We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. 

Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. 

The complete show notes for this episode can be found at https://twimlai.com/go/486.

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Tags:

Ai, artificial intelligence, data, data science, technology, TWiML, tech, machine learning, podcast, ml, Sean Taylor, lyft rideshare labs, experimentation, causality, forecasting, statistics, pytorch, experimental design, model development, fine-grained decision making, heterogeneous treatment effect model, lyft, dragonnet, prophet, open source, neural networks, moonshot, hierarchical modeling

 

Video URL:

https://www.youtube.com/watch?v=5wbLy4SDuo4
 

Video Author:

The TWIML AI Podcast with Sam Charrington

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