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We consider the problem of learning personalized treatment policies that areexternally valid or generalizable: they perform well in other targetpopulations besides the experimental (or training) population from which dataare sampled. We first show that welfare-maximizing policies for theexperimental population are robust to shifts in the distribution of outcomes(but not characteristics) between the experimental and target populations. Wethen develop new methods for learning policies that are robust to shifts inoutcomes and characteristics. In doing so, we highlight how treatment effectheterogeneity within the experimental population affects the generalizabilityof policies. Our methods may be used with experimental or observational data(where treatment is endogenous). Many of our methods can be implemented withlinear programming.

Externally Valid Policy Choice