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

Christopher R Knittel and Samuel Stolper

The Economic Journal, 2025, vol. 135, issue 672, 2377-2401

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ej/ueaf028 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:econjl:v:135:y:2025:i:672:p:2377-2401.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

The Economic Journal is currently edited by Francesco Lippi

More articles in The Economic Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press () and ().