Episode 22 of The Data Science Podcast dives into a fascinating real-world case: how LinkedIn's data science team used causal inference — specifically a method called double machine learning — to figure out whether tweaking the feed algorithm actually caused more engagement, or if the apparent lift was just correlation. Lucas and Luna walk through the problem of confounding variables, the difference between prediction and causation, and how LinkedIn ran a synthetic control-style analysis on historical data instead of a classic A-B test. Along the way, they discuss the limits of observational causal inference, why LinkedIn chose not to run a live experiment, and what other tech platforms can learn from this approach. If you've ever wondered why 'you might also like' recommendations sometimes feel like noise, this episode gives you the data-science reasoning behind the redesign. Features a brief mention of how listener support via buy me a coffee dot com slash fexingo keeps the show ad-free.

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