Capture-Recapture for Casualty Estimation and Beyond: Recent Advances and Research Directions
Statistics in the Public Interest
The most basic quantitative question about the consequences of armed conflicts is perhaps how many people were killed. During and after conflicts, it is common to attempt to create tallies of victims. However, destroyed infrastructure and institutions, danger to field workers, and a reasonable suspicion of data collection by victim communities limit the result of these efforts to incomplete and non-representative lists. Capture-Recapture (CR) estimation, also known as Multiple Systems Estimation (MSE) in the context of human populations, is a family of methods for estimating the size of closed populations based on matched incomplete samples. CR methods vary in details and complexity, but they all ultimately rely on analyzing the patterns of inclusion of individuals across samples to estimate the probability of not being observed and then the number of unobserved individuals. In this discussion, we describe the versions MSE with which analysts have estimated the total number of casualties in armed conflicts. We explore the advances of the last 15 years, and we describe outstanding statistical challenges.
Daniel Manrique-Vallier, Patrick Ball, Mauricio Sadinle. (2022). “Capture-Recapture for Casualty Estimation and Beyond: Recent Advances and Research Directions.” In: Carriquiry, A.L., Tanur, J.M., Eddy, W.F. (eds) Statistics in the Public Interest. Springer Series in the Data Sciences. Springer, Cham. 23 April, 2023. © 2022 Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-75460-0_2