Exploring the Gap Between Causal Inference and Predictive Analytics

With all the LLM hype, it’s worth remembering that enterprise stakeholders want answers to “why” questions. Enter causal inference. Paul Hünermund has been doing research and writing on this topic for some time and joins us to introduce the topic. He also shares some relevant trends and some tips for getting started with methods including double machine learning, experimentation, difference-in-difference, and more.

Join the discussion

Changelog++ members save 3 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

• Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com
• Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. 
• Changelog News – A podcast+newsletter combo that’s brief, entertaining & always on-point. Subscribe today. 

Featuring:

• Paul Hünermund – Website, LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

• How Can Causal Machine Learning Improve Business Decisions?
• Causal Inference is More than Fitting the Data Well
• Causal Data Science in Practice
• Causal Discovery
• DoWhy Github
• The Book of Why
• Causal Data Science Meeting
• Paul’s study on causal ML adoption in industry (incl. an overview of useful software packages in Table 3)
• Causal Data Science MOOC on Udemy

Something missing or broken? PRs welcome!