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A casual conversation concerning causal inference (Practical AI #113)

Podcast |: Changelog Master Feed
Publisher |: Changelog Media
Media Type |: audio
Podknife tags |: Business,; Entrepreneurship,; Interview,; Master Feeds,; Software How-To,; Tech News,; Technology,; Web Development
Categories Via RSS |: Technology
Publication Date |: Nov 24, 2020
Episode Duration |: 00:51:27

Description
Content Encoded

Lucy D'Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris's next Halloween costume).

Lucy D’Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris’s next Halloween costume).

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Featuring:

Lucy D'Agostino McGowan – Twitter, LinkedIn, Website
Chris Benson – Twitter, GitHub, LinkedIn, Website
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Practical AI is a “Media Sponsor” of the R Conference | Government & Public Sector, where Lucy D’Agostino McGowan is giving the talk with Malcolm Barrett called “Causal Inference in R”, as well as a workshop with the same title.

This will be the first ever R Conference focused on data science work in government, defense, and the public sector.

Practical AI listeners get a special discount code valid for 20% off all ticket types, General & Academic Admission and workshops:

PRACTICALAI20

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