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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- Paul Hünermund – Twitter, LinkedIn, Website
- Chris Benson – Twitter, GitHub, LinkedIn, Website
- Daniel Whitenack – Twitter, GitHub, Website
- 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
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(00:00 ) - Welcome to Practical AI
(00:43 ) - Intro to causality & Paul Hünermund
(05:35 ) - Why causality?
(08:11 ) - Determinism vs non-determinism
(11:01 ) - Gaining confidence
(14:06 ) - Sponsor: Changelog News
(15:53 ) - Main ways to use causal inference
(20:09 ) - Making it practical
(22:50 ) - First steps to take
(25:10 ) - Some helpful resources
(27:35 ) - Daniel's practical example
(33:01 ) - The effects of causal learning
(37:11 ) - Closing thoughts
(41:33 ) - Outro