text: 484,18,1443537952626331655,3087935529,Drew Stommes,"In a new working paper with P. M. Aronow and Fredrik Sävje, we reanalyze all studies published in the top journals in political science that use a regression discontinuity (RD) design. <LINK> [1/4] We show that the literature demonstrates some pathological behavior consistent with selective reporting of findings. The figure below demonstrates that findings cluster just at or just above the t-statistic threshold of 1.96 (i.e. a p-value of less than or equal to 0.05). [2/4] <LINK> Reanalysis of these studies using modern, automated methods [see: <LINK>] shows that, while the point estimates are relatively stable, uncertainty has been been systematically understated. [3/4] Retrospective power analyses demonstrate that most studies were underpowered to detect all but large effect sizes. We conclude that many published findings using the RD design are exaggerated if not altogether spurious. [4/4] @guygrossman We included filtering by reviewers/editors in the phrase 'selective reporting.' Perhaps 'selective reporting or publishing' would have been clearer. That said, we do explicitly note publication filtering and selective reporting in the body of the paper.",https://arxiv.org/abs/2109.14526,"The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it the position as a standard method in modern political science research. But identification does not necessarily imply that the causal effects can be estimated accurately with limited data. In this paper, we highlight that estimation is particularly challenging with the RD design and investigate how these challenges manifest themselves in the empirical literature. We collect all RD-based findings published in top political science journals from 2009--2018. The findings exhibit pathological features; estimates tend to bunch just above the conventional level of statistical significance. A reanalysis of all studies with available data suggests that researcher's discretion is not a major driver of these pathological features, but researchers tend to use inappropriate methods for inference, rendering standard errors artificially small. A retrospective power analysis reveals that most of these studies were underpowered to detect all but large effects. The issues we uncover, combined with well-documented selection pressures in academic publishing, cause concern that many published findings using the RD design are exaggerated, if not entirely spurious. ","On the reliability of published findings using the regression