Causal inference

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.

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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!

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Creators & Guests

Host

Chris Benson

Host

Daniel Whitenack

Guest

Paul Hünermund

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!