Abstract: “Unbiased estimates” aren’t really unbiased, for a bunch of reasons, including aggregation, selection, extrapolation, and variation over time. Econometrics typically focus on causal identification, with this goal of estimating “the” effect. But we typically care about individual effects (not “Does the treatment work?” but “Where and when does it work?” and “Where and when does it hurt?”). Estimating individual effects is relevant not only for individuals but also for generalizing to the population. For example, how do you generalize from an A/B test performed on a sample right now to possible effects on a different population in the future? Thinking about variation and generalization can change how we design and analyze experiments and observational studied. We demonstrate with examples in social science and public health.
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