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

Christopher Knittel () and Samuel Stolper

No 26531, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C53 D90 Q40 (search for similar items in EconPapers)
Date: 2019-12
New Economics Papers: this item is included in nep-big, nep-ene and nep-reg
Note: EEE IO LS PE
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