Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
David Randahl, Jonathan P. Williams, H{\aa}vard Hegre

TL;DR
This paper introduces bin-conditional conformal prediction (BCCP), a novel method that provides well-calibrated, region-specific uncertainty estimates for forecasting fatalities in armed conflicts, improving upon standard conformal prediction.
Contribution
The paper presents BCCP, an extension of conformal prediction that ensures consistent coverage across outcome subsets, enhancing local uncertainty estimation in conflict forecasting.
Findings
BCCP achieves well-calibrated local coverage across outcome ranges.
Compared to standard conformal prediction, BCCP produces wider but more accurate prediction intervals.
Application to conflict data demonstrates improved uncertainty quantification.
Abstract
Forecasting armed conflicts is a critical area of research with the potential to save lives and mitigate suffering. While existing forecasting models offer valuable point predictions, they often lack individual-level uncertainty estimates, limiting their usefulness for decision-making. Several approaches exist to estimate uncertainty, such as parametric and Bayesian prediction intervals, bootstrapping, quantile regression, but these methods often rely on restrictive assumptions, struggle to provide well-calibrated intervals across the full range of outcomes, or are computationally intensive. Conformal prediction offers a model-agnostic alternative that guarantees a user-specified level of coverage but typically provides only marginal coverage, potentially resulting in non-uniform coverage across different regions of the outcome space. In this paper, we introduce a novel extension called…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Terrorism, Counterterrorism, and Political Violence
