The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries.

ABSTRACT: Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief tutorial introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model, with emphasis on methodological utility for political science researchers. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival pr

SUBMITTER: Whetten AB 

PROVIDER: S-EPMC8092654 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature