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Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

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  • Long Mark C.

    (School of Public Policy, University of California, Riverside, USA)

  • Rooklyn Jordan

    (Cascade Analysis, Ashland, Oregon, USA)

Abstract

Following Efron (2014), we propose an algorithm for estimating treatment effects for use by researchers employing a regression-discontinuity (RD) design. This algorithm generates a set of estimates of the treatment effect from bootstrapped samples, wherein the polynomial-selection algorithm developed by Pei, Lee, Card, and Weber (2021) is applied to each sample, the average of these RD treatment effect (RDTE) estimates is computed and serves as the overall estimate of the RDTE. Effectively, this procedure estimates a set of plausible RD estimates and weights the estimates by their likelihood of being the best estimate to form a weighted-average estimate. We discuss why this procedure may lower the estimate’s root mean squared error (RMSE). In simulation results, we show that this better performance is achieved, yielding up to a 5% reduction in RMSE relative to PLCW’s method and a 16% reduction in RMSE relative to Calonico, Cattaneo, and Titiunik’s (2014) method for bandwidth selection (with default settings).

Suggested Citation

  • Long Mark C. & Rooklyn Jordan, 2024. "Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-21, January.
    • Handle: RePEc:bpj:causin:v:12:y:2024:i:1:p:21:n:1
    DOI: 10.1515/jci-2022-0028
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    References listed on IDEAS

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    1. Bradley Efron, 2014. "Estimation and Accuracy After Model Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 991-1007, September.
    2. Sebastian Calonico & Matias D. Cattaneo & Rocio Titiunik, 2014. "Robust data-driven inference in the regression-discontinuity design," Stata Journal, StataCorp LP, vol. 14(4), pages 909-946, December.
    3. Andrew Gelman & Guido Imbens, 2019. "Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 447-456, July.
    4. Sebastian Calonico & Matias D Cattaneo & Max H Farrell, 2020. "Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 192-210.
    5. Jens Ludwig & Douglas L. Miller, 2007. "Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(1), pages 159-208.
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