利用机器学习靶向处理：家庭能源使用案例
Using Machine Learning to Target Treatment: The Case of Household Energy Use
The Economic Journal · 2025 · [{"name": "Christopher R. Knittel", "affiliation": ["New School", "Massachusetts Institute of Technology"]}, {"name": "Samuel Stolper", "affiliation": ["New School", "University of Michigan"]}]
中文摘要
摘要：我们检验了因果森林能否通过选择性靶向，提升一项随机化项目的效果，该项目为家庭反复提供旨在促进节能的行为助推。该项目的平均处理效应为每月用电量减少9千瓦时（kWh），但预测减少量的完整分布范围约为1至33千瓦时。处理前用电量和住房价值是差异化处理效应的最强预测变量。在两项靶向练习中，相对于现状，使用因果森林使助推项目的社会净收益提高了3至5倍。使用以较早项目波次校准的模型来选择较晚波次中的靶向家庭，我们估计该因果森林产生的收益高于其他五种替代预测模型。通过自助法生成置信区间，我们发现，相对于其中部分（但非全部）替代模型，因果森林的优势具有统计显著性。
Abstract
Abstract We test the ability of causal forests to improve, through selective targeting, the effectiveness of a randomised program providing repeated behavioural nudges towards household energy conservation. The average treatment effect of the program is a monthly electricity reduction of 9 kilowatt hours (kWh), but the full distribution of predicted reductions ranges from roughly 1 to 33 kWh. Pre-treatment electricity consumption and home value are the strongest predictors of differential treatment effects. In a pair of targeting exercises, use of the causal forest increases social net benefits of the nudge program by a factor of 3–5 relative to the status quo. Using models calibrated with earlier program waves to choose households to target in later ones, we estimate that the forest produces more benefits than five other alternative predictive models. Bootstrapping to generate confidence intervals, we find the forest’s advantage to be statistically significant relative to some, but not all, of these alternatives.