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Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning. (arXiv:2211.12004v1 [econ.EM])
Nov. 23, 2022, 2:13 a.m. | Susan Athey, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams
stat.ML updates on arXiv.org arxiv.org
We design and implement an adaptive experiment (a ``contextual bandit'') to
learn a targeted treatment assignment policy, where the goal is to use a
participant's survey responses to determine which charity to expose them to in
a donation solicitation. The design balances two competing objectives:
optimizing the outcomes for the subjects in the experiment (``cumulative regret
minimization'') and gathering data that will be most useful for policy
learning, that is, for learning an assignment rule that will maximize welfare
if …
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