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

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, WebsiteChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website

Show Notes:

Casual Inference Podcast
Casual Inference Podcast | Twitter
Communicating Complex Statistics (video)
Communicating Complex Statistics (slides)

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

Something missing or broken? PRs welcome!