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Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs
Abstract
It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.
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“we show that the results are robust to using linear and quadratic regressions, as proposed by Gelman and Imbens (53)”
From Collective property rights reduce deforestation in the Brazilian Amazon (2020) · cites this paper as Gelman and Imbens [53]
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Cites
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Self-citationRegression-discontinuity analysis: An alternative to the ex post facto experiment.
Thistlethwaite, D. L., & Campbell, D. T. (1960) · Journal of Educational Psychology · doi:10.1037/h0044319 · Cited by 1,469
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A Survey of Learning Causality with Data: Problems and Methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2021) · ACM Computing Surveys · doi:10.1145/3397269 · Cited by 362
Collective property rights reduce deforestation in the Brazilian Amazon
Baragwanath, K., & Bayi, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917874117 · Cited by 230
Spreading Uncertainty, Shrinking Birth Rates: A Natural Experiment for Italy
Comolli, C. L., & Vignoli, D. (2021) · European Sociological Review · doi:10.1093/esr/jcab001 · Cited by 172
Segmentary Lineage Organization and Conflict in Sub-Saharan Africa
Moscona, J., Nunn, N., & Robinson, J. A. (2020) · Econometrica · doi:10.3982/ecta16327 · Cited by 108
Chu, Y.-W. L., & Cuffe, H. E. (2024) · Review of Economics and Statistics · doi:10.1162/rest_a_01144 · Cited by 8
Metadata
- Authors
- Gelman, A., & Imbens, G.
- Affiliations
- Department of Statistics and Department of Political Science, Columbia University, New York, NY, 10027 ()
- Graduate School of Business, Stanford University, Stanford, CA 94305
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2018-05-14
- Volume
- 37
- Issue
- 3
- Pages
- 447-456
- ISSN
- 0735-0015
- 1537-2707
- Cited by
- 1,101
- References
- 2
- Language
- en
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