全球智库信息集成服务系统(G2TT): Using Machine Learning to Target Treatment: The Case of Household Energy Use

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w26531
来源ID	Working Paper 26531
	Using Machine Learning to Target Treatment: The Case of Household Energy Use
	Christopher R. Knittel; Samuel Stolper
发表日期	2019-12-09
出版年	2019
语种	英语
摘要	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.
主题	Econometrics ; Estimation Methods ; Microeconomics ; Behavioral Economics ; Environmental and Resource Economics ; Energy
URL	https://www.nber.org/papers/w26531
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/584205
推荐引用方式 GB/T 7714	Christopher R. Knittel,Samuel Stolper. Using Machine Learning to Target Treatment: The Case of Household Energy Use. 2019.