  A crucial challenge for solving problems in conflict research is in
leveraging the semi-supervised nature of the data that arise. Observed response
data such as counts of battle deaths over time indicate latent processes of
interest such as intensity and duration of conflicts, but defining and labeling
instances of these unobserved processes requires nuance and imprecision. The
availability of such labels, however, would make it possible to study the
effect of intervention-related predictors - such as ceasefires - directly on
conflict dynamics (e.g., latent intensity) rather than through an intermediate
proxy like observed counts of battle deaths. Motivated by this problem and the
new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose
a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a
sufficiently flexible machine learning approach for semi-supervised regime
labeling with uncertainty quantification. We motivate our approach by
illustrating the way it can be used to study the role that ceasefires play in
shaping conflict dynamics. This ceasefires data set is the first systematic and
globally comprehensive data on ceasefires, and our work is the first to analyze
this new data and to explore the effect of ceasefires on conflict dynamics in a
comprehensive and cross-country manner.
