Supercharge A/B testing with automated causal inference | Community Workshop on Microsoft’s Causal Tools
Microsoft
Supercharge A/B testing with automated causal inference | Community Workshop on Microsoft’s Causal Tools
An A/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating […]
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