(Machine) Learning What Policies Value

@article{Bjrkegren2022MachineLW,
  title={(Machine) Learning What Policies Value},
  author={Daniel Bj{\"o}rkegren and Joshua Evan Blumenstock and Samsun Knight},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.00727}
}
When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We use machine learning methods to estimate how much each individual benefits from an intervention, and then reconcile its allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes… 

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