Cover Image for Experimentation + Causal Inference Debate
Cover Image for Experimentation + Causal Inference Debate
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"All useful analysis implies something causal."

This quote from Erik Gregory captures why this debate is so important. Data Scientists don't do analysis just for research; we do it to drive action. And only causal analysis can confidently tell us that doing X is better than doing Y.

This leads to a high-stakes dilemma every data leader faces: Do we wait for the ground truth from an A/B test, or do we move faster with an observational study? This choice isn't academic—it's the multi-billion dollar question our guests from Amazon and Meta have had to answer at a scale few can imagine.

Tonight, we’re cutting through the noise. We're not here for platitudes. We're here for a direct debate on the real-world friction, the hidden costs, and the breaking points of both approaches. Get ready for the war stories and frameworks you can quote at work on Monday morning.

Cast

  • Chris Khawand – The godfather of observational causal inference at Amazon. His model allocates billions of dollars.

  • John Meakin – One of the most influential data scientists at Meta, known for scaling experimentation analysis and driving mega-million incremental values.

  • Svet Semov – The only data scientist who has worked on both Amazon’s and Meta’s central experimentation teams.

Moderator: Yuzheng Sun, Statsig

Location
14725 SE 36th St #200
Bellevue, WA 98006, USA
Statsig HQ
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1,243 Went