Super Data Science: ML & AI Podcast with Jon Krohn cover image

Super Data Science: ML & AI Podcast with Jon Krohn

607: Inferring Causality

Sep 6, 2022
Professor Jennifer Hill from NYU discusses causality, correlation vs. causation, counterfactuals, Bayesian and ML tools for causal inferences, and a new GUI for causal inferences. Tips on learning more about causal inference, the usefulness of multilevel models, and clarifying assumptions when inferring causality from data are also covered.
01:13:12

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Understanding causality is pivotal in data science decision-making.
  • Differentiating correlation from causation requires randomization for confident causal inferences.

Deep dives

Importance of Causality in Data Science

Understanding causality is crucial in data science applications. Professor Jennifer Hill discusses how causal questions drive decision-making, emphasizing that many decisions are based on implicit causal reasoning. From designing research to implementing causal models, causality plays a central role in all data science applications.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode