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

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  • Christopher R. Knittel
  • Samuel Stolper

Abstract

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.

Suggested Citation

  • Christopher R. Knittel & Samuel Stolper, 2019. "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers 26531, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26531
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    Cited by:

    1. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
    2. Chang Cai & Sandy Dall’Erba, 2021. "On the evaluation of heterogeneous climate change impacts on US agriculture: does group membership matter?," Climatic Change, Springer, vol. 167(1), pages 1-23, July.
    3. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
    4. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    5. Andor, Mark A. & Gerster, Andreas & Peters, Jörg, 2022. "Information campaigns for residential energy conservation," European Economic Review, Elsevier, vol. 144(C).
    6. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    7. Bernard, René, 2022. "Mental Accounting and the Marginal Propensity to Consume," VfS Annual Conference 2022 (Basel): Big Data in Economics 264186, Verein für Socialpolitik / German Economic Association.
    8. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
    9. Saunders, Harry D. & Roy, Joyashree & Azevedo, Inês M.L. & Chakravarty, Debalina & Dasgupta, Shyamasree & De La Rue Du Can, Stephane & Druckman, Angela & Fouquet, Roger & Grubb, Michael & Lin, Boqiang, 2021. "Energy efficiency: what has research delivered in the last 40 years?," LSE Research Online Documents on Economics 114344, London School of Economics and Political Science, LSE Library.
    10. Jun Mao & Jiahao Xie & Zunguo Hu & Lijie Deng & Haitao Wu & Yu Hao, 2023. "Sustainable development through green innovation and resource allocation in cities: Evidence from machine learning," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(4), pages 2386-2401, August.
    11. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    12. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.
    13. Anya Shchetkina & Ron Berman, 2024. "When Is Heterogeneity Actionable for Personalization?," Papers 2411.16552, arXiv.org.
    14. Bernard, René, 2023. "Mental accounting and the marginal propensity to consume," Discussion Papers 13/2023, Deutsche Bundesbank.
    15. Papineau, Maya & Rivers, Nicholas, 2022. "Experimental evidence on heat loss visualization and personalized information to motivate energy savings," Journal of Environmental Economics and Management, Elsevier, vol. 111(C).
    16. Sylvia Klosin & Max Vilgalys, 2022. "Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application," Papers 2207.08789, arXiv.org, revised Sep 2023.
    17. Fabra, Natalia & Lacuesta, Aitor & Souza, Mateus, 2022. "The implicit cost of carbon abatement during the COVID-19 pandemic," European Economic Review, Elsevier, vol. 147(C).
    18. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    19. Brick, Kerri & De Martino, Samantha & Visser, Martine, 2023. "Behavioural nudges for water conservation in unequal settings: Experimental evidence from Cape Town," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    20. Yujie Xu & Vivian Loftness & Edson Severnini, 2021. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio," Energies, MDPI, vol. 14(14), pages 1-24, July.
    21. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    22. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    23. Hunt Allcott & Daniel Cohen & William Morrison & Dmitry Taubinsky, 2022. "When do "Nudges" Increase Welfare?," NBER Working Papers 30740, National Bureau of Economic Research, Inc.
    24. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    25. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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