Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects
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 develope...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-02-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2022-0028 |
| _version_ | 1827330738509316096 |
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| author | Long Mark C. Rooklyn Jordan |
| author_facet | Long Mark C. Rooklyn Jordan |
| author_sort | Long Mark C. |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-07T16:20:46Z |
| format | Article |
| id | doaj.art-603e83f7914144ed99359c0deda9f982 |
| institution | Directory Open Access Journal |
| issn | 2193-3685 |
| language | English |
| last_indexed | 2024-03-07T16:20:46Z |
| publishDate | 2024-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-603e83f7914144ed99359c0deda9f9822024-03-04T07:29:03ZengDe GruyterJournal of Causal Inference2193-36852024-02-01121991100710.1515/jci-2022-0028Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effectsLong Mark C.0Rooklyn Jordan1School of Public Policy, University of California, Riverside, USACascade Analysis, Ashland, Oregon, USAFollowing 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).https://doi.org/10.1515/jci-2022-0028data-driven algorithmregression discontinuitybootstrap62j0562f4062j20 |
| spellingShingle | Long Mark C. Rooklyn Jordan Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects Journal of Causal Inference data-driven algorithm regression discontinuity bootstrap 62j05 62f40 62j20 |
| title | Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects |
| title_full | Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects |
| title_fullStr | Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects |
| title_full_unstemmed | Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects |
| title_short | Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects |
| title_sort | regression s discontinuity using bootstrap aggregation to yield estimates of rd treatment effects |
| topic | data-driven algorithm regression discontinuity bootstrap 62j05 62f40 62j20 |
| url | https://doi.org/10.1515/jci-2022-0028 |
| work_keys_str_mv | AT longmarkc regressionsdiscontinuityusingbootstrapaggregationtoyieldestimatesofrdtreatmenteffects AT rooklynjordan regressionsdiscontinuityusingbootstrapaggregationtoyieldestimatesofrdtreatmenteffects |