Heterogeneous Treatment Effects
When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what theyโre usually finding is the so-called average treatment effect. In other words, on average, hereโs what the treatment does in terms of making a certain outcome more or less likely to happen. But thereโs more to life than averages: sometimes the relationship works one way in some cases, and another way in other cases, such that the average isnโt 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/