Christina Korting, Carl Lieberman, Jordan Matsudaira, Zhuan Pei, Yi Shen
arXiv 6 Dec 2021 · Econometrics · publishedThe Quarterly Journal of Economics (2023) · 27 citations (OpenAlex)
arXiv:2112.03096 · PDF · DOI · OpenAlex · Extracted main text
Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Our experimental results allow us to make evidence-based recommendations to practitioners, and we suggest using small bins with no fit lines as a starting point to construct RD graphs. Second, we compare visual inference on graphs constructed using our preferred method with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error (false positive) rates and complements econometric inference.
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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 | Lee, David S. and Thomas Lemieux (2010) Regression Discontinuity Designs in Economics | 1.000 | 6 | 3 | 100% |
| 2 | Majumder, Mahbubul, Heike Hofmann, and Dianne Cook (2013) Validation of Visual Statistical Inference, Applied to Linear Models | 1.000 | 5 | 4 | 100% |
| 3 | Armstrong, Timothy B and Michal Kolesár (2018) Optimal Inference in a Class of Regression Models | 0.888 | 10 | 6 | 70% |
| 4 | Imbens, Guido and Karthik Kalyanaraman (2012) Optimal Bandwidth Choice for the Regression Discontinuity Estimator | 0.860 | 11 | 7 | 64% |
| 5 | Korting, Christina, Carl Lieberman, Jordan Matsudaira, Zhuan Pei, an… (2020) Visual Inference and Graphical Representation in Regression Discontinuity Designs self | 0.843 | 4 | 3 | 75% |
| 6 | Calonico, Sebastian, Matias D. Cattaneo, and Rocio Titiunik (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs | 0.830 | 14 | 8 | 57% |
| 7 | Andrews, Isaiah and Jesse M Shapiro (2021) A Model of Scientific Communication | 0.737 | 5 | 5 | 40% |
| 8 | Pei, Zhuan, David S. Lee, David Card, and Andrea Weber (2022) Local Polynomial Order in Regression Discontinuity Designs self | 0.737 | 3 | 3 | 67% |
| 9 | Calonico, Sebastian, Matias D. Cattaneo, and Rocio Titiunik (2015) Optimal Data-Driven Regression Discontinuity Plots | 0.685 | 59 | 8 | 32% |
| 10 | Armstrong, Timothy B and Michal Kolesár (2020) Simple and Honest Confidence Intervals in Nonparametric Regression | 0.644 | 4 | 2 | 50% |
Showing the top 10 of 81 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | On Binscatter | 0.405 | 1 | 1 |
| 2 | Regression Discontinuity Designs | 0.405 | 1 | 1 |
| 3 | A unified test for regression discontinuity designs | 0.405 | 1 | 1 |
| 4 | A Guide to Regression Discontinuity Designs in Medical Applications | 0.405 | 1 | 1 |