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

Citations

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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. Chen, Chen & Xue, Zhixin, 2025. "New-type infrastructure and corporate digital transformation: Evidence from a multimethod machine learning approach," Finance Research Letters, Elsevier, vol. 74(C).
  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. Hirano, Keisuke & Porter, Jack R., 2020. "Asymptotic analysis of statistical decision rules in econometrics," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 283-354, Elsevier.
  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. Jesper Akesson & Robert Hahn & Rajat Kochhar & Robert Metcalfe, 2025. "Do Water Audits Work?," Natural Field Experiments 00820, The Field Experiments Website.
  12. 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.
  13. 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.
  14. 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.
  15. repec:arx:papers:2411.16552 is not listed on IDEAS
  16. Bernard, René, 2023. "Mental accounting and the marginal propensity to consume," Discussion Papers 13/2023, Deutsche Bundesbank.
  17. 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).
  18. 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 Oct 2025.
  19. 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).
  20. 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.
  21. 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.
  22. 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).
  23. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2025. "A Machine Learning Approach to Analyze and Support Anticorruption Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 17(2), pages 162-193, May.
  24. 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.
  25. Hunt Allcott & Daniel Cohen & William Morrison & Dmitry Taubinsky, 2022. "When do "Nudges" Increase Welfare?," NBER Working Papers 30740, National Bureau of Economic Research, Inc.
  26. Harald Mayr & Mateus Souza, 2025. "The Tragedy of the Common Heating Bill," CRC TR 224 Discussion Paper Series crctr224_2025_629v2, University of Bonn and University of Mannheim, Germany, revised Oct 2025.
  27. 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.
  28. 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).
  29. 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).
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