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 discussionChangelog++ 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, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow 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!

Paul Hünermund, Assistant Professor at Copenhagen Business School, discusses the potential of causal AI in business. Topics covered include validating AI output to avoid hallucinations, the distinction between causal AI and traditional causal statistical techniques, diverse applications in business, the Google controversy, techniques and tools of causal AI, the role of expert domain knowledge, understanding causal models and inference techniques.

Paul Hünermund, assistant professor of Strategy and Innovation, discusses why the Nobel Prize in Economics was awarded to David Card, Joshua Angrist, and Guido Imbens. They explore the revolutionary framework of natural experiments and the practical implications of their research in labor markets, education, and business decision-making. The podcast also delves into the role of machine learning in causal analysis and invites feedback and subscription.