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Surrogate index approaches have recently become a popular method ofestimating longer-term impact from shorter-term outcomes. In this paper, weleverage 1098 test arms from 200 A/B tests at Netflix to empiricallyinvestigate to what degree would decisions made using a surrogate indexutilizing 14 days of data would align with those made using direct measurementof day 63 treatment effects. Focusing specifically on linear "auto-surrogate"models that utilize the shorter-term observations of the long-term outcome ofinterest, we find that the statistical inferences that we would draw from usingthe surrogate index are ~95% consistent with those from directly measuring thelong-term treatment effect. Moreover, when we restrict ourselves to the set oftests that would be "launched" (i.e. positive and statistically significant)based on the 63-day directly measured treatment effects, we find that relyinginstead on the surrogate index achieves 79% and 65% recall.

Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix