Research Reproducibility as a Survival Analysis


  • Edward Raff Booz Allen Hamilton University of Maryland, Baltimore County


Software Engineering


There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at




How to Cite

Raff, E. (2021). Research Reproducibility as a Survival Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 469-478. Retrieved from



AAAI Technical Track on Application Domains