Episode 79 of The Data Science Podcast explores synthetic control — a causal inference method that estimates what would have happened to a treated unit if the intervention never occurred. Lucas and Luna break down a real-world case: how a ride-hailing company used synthetic control to measure the impact of a surge-pricing algorithm change on driver supply in Austin, Texas. They walk through building a synthetic control from a weighted combination of similar cities, interpreting the gap between actual and synthetic outcomes, and running placebo tests to assess statistical significance. The hosts also discuss when to choose synthetic control over difference-in-differences, the importance of having a strong donor pool, and how this method is gaining traction in policy evaluation and A/B testing for large-scale platform changes. No clickbait, just a practical, concrete guide to a powerful causal technique.

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