Art B. Owen, Hal Varian
arXiv 22 Aug 2018 · Statistics — Methodology · publishedElectronic Journal of Statistics (2020) · 1 citations (OpenAlex)
arXiv:1808.07563 · PDF · DOI · OpenAlex · Extracted main text
Motivated by customer loyalty plans and scholarship programs, we study tie-breaker designs which are hybrids of randomized controlled trials (RCTs) and regression discontinuity designs (RDDs). We quantify the statistical efficiency of a tie-breaker design in which a proportion $\Delta$ of observed subjects are in the RCT. In a two line regression, statistical efficiency increases monotonically with $\Delta$, so efficiency is maximized by an RCT. We point to additional advantages of tie-breakers versus RDD: for a nonparametric regression the boundary bias is much less severe and for quadratic regression, the variance is greatly reduced. For a two line model we can quantify the short term value of the treatment allocation and this comparison favors smaller $\Delta$ with the RDD being best. We solve for the optimal tradeoff between these exploration and exploitation goals. The usual tie-breaker design applies an RCT on the middle $\Delta$ subjects as ranked by the assignment variable. We quantify the efficiency of other designs such as experimenting only in the second decile from the top. We also show that in some general parametric models a Monte Carlo evaluation can be replaced by matrix algebra.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Robin Tepper Jacob, Pei Zhu, Marie-Andrée, Somers, and Howard Bloom (2012) A practical guide to regression discontinuity | 0.811 | 4 | 2 | 100% |
| 2 | A. S. Goldberger (1972) Selection bias in evaluating treatment effects: Some formal illustrations | 0.737 | 3 | 2 | 100% |
| 3 | Guido W Imbens and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 0.737 | 3 | 2 | 100% |
| 4 | Andrew Gelman and Guido Imbens (2017) Why high-order polynomials should not be used in regression discontinuity designs | 0.511 | 2 | 1 | 100% |
| 5 | Joshua D. Angrist and Jorn-Steffen Pischke (2009) Mostly Harmless Econometrics | 0.405 | 1 | 1 | 100% |
| 6 | Joshua D. Angrist and Jorn-Steffen Pischke (2014) Mastering Metrics | 0.405 | 1 | 1 | 100% |
| 7 | Joshua Angrist, Sally Hudson, and Amanda Pallais (2014) Leveling up: Early results from a randomized evaluation of post-secondary aid | 0.405 | 1 | 1 | 100% |
| 8 | Joseph C. Cappelleri and William M. K. Trochim (2003) Cutoff designs | 0.405 | 1 | 1 | 100% |
| 9 | Guido Imbens and Thomas Lemieux (2008) Regression discontinuity designs: a guide to practice | 0.405 | 1 | 1 | 100% |
| 10 | Wilbert Van Der Klaauw (2008) Regression–discontinuity analysis: A survey of recent developments in economics | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A general characterization of optimal tie-breaker designs | 1.000 | 14 | 5 |
| 2 | Multivariate Tie-breaker Designs | 1.000 | 5 | 3 |
| 3 | On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach | 0.405 | 1 | 1 |
| 4 | Regression Discontinuity Designs | 0.405 | 1 | 1 |
| 5 | Double machine learning and design in batch adaptive experiments | 0.405 | 1 | 1 |