Using Machine Learning to Target Treatment: The Case of Household Energy Use
收藏NBER2019-12-01 更新2025-01-04 收录
下载链接：
https://www.nber.org/papers/w26531
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资源简介：
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.
提供机构：
美国国家经济研究局
创建时间：
2019-12-01
