@article{5ca54923b8cb4ed4a094d8852abee318,
title = "Precluding rare outcomes by predicting their absence",
abstract = "Forecasting extremely rare events is a pressing problem, but efforts to model such outcomes are often limited by the presence of multiple causes within classes of events, insufficient observations of the outcome to assess fit, and biased estimates due to insufficient observations of the outcome. We introduce a novel approach for analyzing rare event data that addresses these challenges by turning attention to the conditions under which rare outcomes do not occur. We detail how configurational methods can be used to identify conditions or sets of conditions that would preclude the occurrence of a rare outcome. Results from Monte Carlo experiments show that our approach can be used to systematically preclude up to 78.6% of observations, and application to ground-truth data coupled with a bootstrap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses.",
author = "Schoon, {Eric W.} and David Melamed and Breiger, {Ronald L.} and Eunsung Yoon and Christopher Kleps",
note = "Funding Information: This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2018–17121900006. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. There was no additional external funding received for this study. Publisher Copyright: {\textcopyright} 2019 Schoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
year = "2019",
month = oct,
day = "1",
doi = "10.1371/journal.pone.0223239",
language = "English (US)",
volume = "14",
journal = "PloS one",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "10",
}