Agents that work on the basis of behavioral data need to be able to make judgements about how successful (or not) their actions are. Unlike an A/B test or a multi-armed bandit, where you use success rates over many users to determine the relative value of different options, an agent needs to be able to try one specific action with one specific user and make a judgement call about whether that action inclined the user in the right direction. There's not such thing as a success rate when you're dealing with a single intervention for a single user. Instead, Aampe agents use a version of Interrupted Time Series analysis, simplified for use with sparse data.

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