Visual Inference and Graphical Representation in Regression Discontinuity Designs

Christina Korting, Carl Lieberman, Jordan Matsudaira, Zhuan Pei and Yi Shen ()
Additional contact information
Christina Korting: University of Delaware
Jordan Matsudaira: Columbia University
Yi Shen: University of Waterloo

No 14923, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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. Incorporating the experimental results into two decision theoretical criteria adapted from the recent economics literature, we find that using small bins with no fit lines to construct RD graphs performs well and recommend it as a starting point to practitioners. Second, we compare visual inference with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error rates and complements econometric inference.

Keywords: statistical decision theory; expert prediction; regression discontinuity design; visual inference; graphical methods; scientific communication (search for similar items in EconPapers)
JEL-codes: A11 C10 C40 (search for similar items in EconPapers)
Pages: 102 pages
Date: 2021-12
New Economics Papers: this item is included in nep-exp
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Published - published in: Quarterly Journal of Economics, 2023, 138 (3), 1977–2019,

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Related works:
Journal Article: Visual Inference and Graphical Representation in Regression Discontinuity Designs (2023)
Working Paper: Visual Inference and Graphical Representation in Regression Discontinuity Designs (2023)
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