Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use
Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use
复制标题
关于治疗效果异质性的机器学习：以家庭能源使用为例
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Stolper
中科院分区：
文献类型：
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作者：
Christopher R. Knittel;S. Stolper
我们使用因果森林评估异质性的治疗效果（TE）的重复行为轻推对家庭节能。对治疗的平均反应是每月减少9千瓦时（kWh）的电力，但反应的全部分布范围从-40到+10 kWh不等。随着时间的推移，家庭学会减少更多，条件是在第一年做出反应。治疗前消费和家庭价值是森林中最常用的预测因素。研究结果表明，使用机器学习技术来改进治疗的靶向和定制的能力。
We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges toward household energy conservation. The average response to treatment is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -40 to +10 kWh. Households learn to reduce more over time, conditional on having responded in year one. Pre-treatment consumption and home value are the most commonly used predictors in the forest. The results suggest the ability to use machine learning techniques for improved targeting and tailoring of treatment.