Todd Gerarden, Cornell

We investigate the potential for targeted treatment rules to improve the cost-effectiveness of a large-scale behavioral intervention to encourage energy conservation. We first verify that receiving personalized feedback on energy use causes households to reduce their electricity consumption on average and present evidence of treatment effect heterogeneity. We then use a policy learning approach to derive treatment rules based on observable household characteristics that exploit this heterogeneity to maximize the expected benefits of the intervention. Targeting using transparent and easily implemented treatment rules yields significant gains relative to the randomized treatment assignment that was actually implemented.

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