Heterogeneous Treatment Effects
Published January 20, 2019
17 min
    Add to queue
    Copy URL
    Show notes
    When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what they’re usually finding is the so-called average treatment effect. In other words, on average, here’s what the treatment does in terms of making a certain outcome more or less likely to happen. But there’s 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’t 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. Relevant links: https://eng.uber.com/analyzing-experiment-outcomes/ https://multithreaded.stitchfix.com/blog/2018/11/08/bandits/ https://www.locallyoptimistic.com/post/against-ab-tests/
        0:00:00 / 0:00:00