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Causal inference (Practical AI #220)

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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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