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Forecasted Treatment Effects

Author

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  • Irene Botosaru
  • Raffaella Giacomini
  • Martin Weidner

Abstract

We consider estimation and inference of the effects of a policy in the absence of a control group. We obtain unbiased estimators of individual (heterogeneous) treatment effects and a consistent and asymptotically normal estimator of the average treatment effect. Our estimator averages over unbiased forecasts of individual counterfactuals, based on a (short) time series of pre-treatment data. The paper emphasizes the importance of focusing on forecast unbiasedness rather than accuracy when the end goal is estimation of average treatment effects. We show that simple basis function regressions ensure forecast unbiasedness for a broad class of data-generating processes for the counterfactuals, even in short panels. In contrast, model-based forecasting requires stronger assumptions and is prone to misspecification and estimation bias. We show that our method can replicate the findings of some previous empirical studies, but without using a control group.

Suggested Citation

  • Irene Botosaru & Raffaella Giacomini & Martin Weidner, 2023. "Forecasted Treatment Effects," Papers 2309.05639, arXiv.org, revised Jan 2024.
    • Handle: RePEc:arx:papers:2309.05639
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    References listed on IDEAS

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    1. Gallego, Francisco & Montero, Juan-Pablo & Salas, Christian, 2013. "The effect of transport policies on car use: A bundling model with applications," Energy Economics, Elsevier, vol. 40(S1), pages 85-97.
    2. Marc K. Chan & Simon S. Kwok, 2022. "The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1216-1233, June.
    3. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    4. Gallego, Francisco & Montero, Juan-Pablo & Salas, Christian, 2013. "The effect of transport policies on car use: Evidence from Latin American cities," Journal of Public Economics, Elsevier, vol. 107(C), pages 47-62.
    5. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    6. Tu, Meng & Zhang, Bing & Xu, Jianhua & Lu, Fangwen, 2020. "Mass media, information and demand for environmental quality: Evidence from the “Under the Dome”," Journal of Development Economics, Elsevier, vol. 143(C).
    7. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    8. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
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    Cited by:

    1. Dubé, Jean & Le Gallo, Julie & Des Rosiers, François & Legros, Diègo & Champagne, Marie-Pier, 2024. "An integrated causal framework to evaluate uplift value with an example on change in public transport supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).

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