There is increasing interest in using observed individual-level datato formulate personalized policy. Examples of this include heterogeneous pricing, individualized credit offers, and targeted social programs. This paper provides a general model of how personalized policy creates incentives for individuals to modify their behavior to obtain a better treatment. For a given planner objective, we show that standard estimators based on repeated risk minimization produce a suboptimal policy . We propose a dynamic experiment that estimates the optimal treatment allocation function when agents are strategic and has regret that decays at a linear rate. A key insight is that random variation in how treatment assignment depends on observed characteristics is required, and that randomized treatment assignment alone is not sufficient to identify the optimal policy. We show this experimental method outperforms alternative methods that do not learn strategic effects in simulations and in a small MTurk experiment.
There is increasing interest in allocating treatments based on observed individual data: examples include heterogeneous pricing, individualized credit offers, and targeted social programs. Personalized policy introduces incentives for individuals to modify their behavior to obtain a better treatment. We show standard risk minimization-based estimators are sub-optimal when observed covariates are endogenous to the treatment allocation rule . We propose a dynamic experiment that converges to the optimal treatment allocation function without parametric assumptions on individual strategic behavior, and prove that it has regret that decays at a linear rate. We validate the method in simulations and in a small MTurk experiment.
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using observed individual-level datato formulate personalized policy. Examples of this
allocating treatments based on observed individual data: examples
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This paper provides a general model of how personalized policy creates
Personalized policy introduces
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For a given planner objective, we show that standard estimators based on repeated risk minimization produce a suboptimal policy
We show standard risk minimization-based estimators are sub-optimal when observed covariates are endogenous to the treatment allocation rule
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estimates
converges to
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when agents are strategic and
without parametric assumptions on individual strategic behavior, and prove that it
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A key insight is that random variation in how treatment assignment depends on observed characteristics is required, and that randomized treatment assignment alone is not sufficient to identify the optimal policy. We show this experimental method outperforms alternative methods that do not learn strategic effects
We validate the method