KDI 한국개발연구원 - 경제정책정보 - 국내외연구자료 주제 - 경제일반 - 경제일반 -

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Using Machine Learning to Target Treatment: The Case of Household Energy Use

NBER 2020.01.02
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We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges towards household energy conservation. The average response is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -30 to +10 kWh. Selective targeting of treatment using the forest raises social net benefits by 12-120 percent, depending on the year and welfare function. Pre-treatment consumption and home value are the strongest predictors of treatment effect. We find suggestive evidence of a "boomerang effect": households with lower consumption than similar neighbors are the ones with positive TE estimates.

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