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Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes

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  • Eoghan O'Neill
  • Melvyn Weeks

Abstract

We examine the household-specific effects of the introduction of Time-of-Use (TOU) electricity pricing schemes. Using a causal forest (Athey and Imbens, 2016; Wager and Athey, 2018; Athey et al., 2019), we consider the association between past consumption and survey variables, and the effect of TOU pricing on household electricity demand. We describe the heterogeneity in household variables across quartiles of estimated demand response and utilise variable importance measures. Household-specific estimates produced by a causal forest exhibit reasonable associations with covariates. For example, households that are younger, more educated, and that consume more electricity, are predicted to respond more to a new pricing scheme. In addition, variable importance measures suggest that some aspects of past consumption information may be more useful than survey information in producing these estimates.

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  • Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
    • Handle: RePEc:arx:papers:1810.09179
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Valeria Di Cosmo, Sean Lyons, and Anne Nolan, 2014. "Estimating the Impact of Time-of-Use Pricing on Irish Electricity Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    3. Cosmo, Valeria Di & O’Hora, Denis, 2017. "Nudging electricity consumption using TOU pricing and feedback: evidence from Irish households," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 1-14.
    4. Brian C. Prest, 2020. "Peaking Interest: How Awareness Drives the Effectiveness of Time-of-Use Electricity Pricing," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(1), pages 103-143.
    5. Jonathan M.V. Davis & Sara B. Heller, 2017. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," NBER Working Papers 23443, National Bureau of Economic Research, Inc.
    6. Bollinger, Bryan & Hartmann, Wesley R., 2015. "Welfare Effects of Home Automation Technology with Dynamic Pricing," Research Papers 3274, Stanford University, Graduate School of Business.
    7. Matthew Harding & Carlos Lamarche, 2016. "Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time‐of‐Use Electricity Pricing Policies," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(4), pages 906-931, September.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    9. Lu Tian & Ash A. Alizadeh & Andrew J. Gentles & Robert Tibshirani, 2014. "A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1517-1532, December.
    10. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
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