Visual Inference and Graphical Representation in Regression Discontinuity Designs*

• 成果类型: Article
• 署名作者: Korting, Christina; Lieberman, Carl; Matsudaira, Jordan; Pei, Zhuan; Shen, Yi
• 署名单位: University of Delaware; Columbia University; Cornell University; IZA Institute Labor Economics
• 刊物名称: QUARTERLY JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0033-5533
• DOI: 10.1093/qje/qjad011
• 发表日期: 2023
• 页码: 1977-2019
• 关键词: OPTIMAL BANDWIDTH CHOICE; confidence-intervals; guide; MODEL

摘要

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 in 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 effects 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.