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

Autor:	Samuel Stolper, Christopher R. Knittel
Rok vydání:	2019
Předmět:	Consumption (economics) business.industry media_common.quotation_subject Energy (esotericism) Distribution (economics) Boomerang effect Energy conservation Value (economics) Economics Econometrics Treatment effect business Welfare media_common
DOI:	10.3386/w26531
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::249e42cc24f0f1a6bc3e870e90181f79 https://doi.org/10.3386/w26531 Zobrazit plný text záznamu