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

Author

Listed:
  • 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 (2011) 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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