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When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what they\u2019re usually finding is the so-called average treatment effect. In other words, on average, here\u2019s what the treatment does in terms of making a certain outcome more or less likely to happen. But there\u2019s more to life than averages: sometimes the relationship works one way in some cases, and another way in other cases, such that the average isn\u2019t giving you the whole story. In that case, you want to start thinking about heterogeneous treatment effects, and this is the podcast episode for you.\n\nRelevant links:\nhttps://eng.uber.com/analyzing-experiment-outcomes/\nhttps://multithreaded.stitchfix.com/blog/2018/11/08/bandits/\nhttps://www.locallyoptimistic.com/post/against-ab-tests/