Forecasting Civil Wars: Theory and Structure in an Age of “Big Data” and Machine Learning

Blair, Robert A.; Sambanis, Nicholas

doi:10.1177/0022002720918923

Cited by 52Political Conflict and GovernancePeacebuilding and International SecurityQualitative Comparative Analysis Research

Forecasting Civil Wars: Theory and Structure in an Age of “Big Data” and Machine Learning

Blair, Robert A., Sambanis, Nicholas·2020·Journal of Conflict Resolution

DOI 10.1177/0022002720918923

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Abstract

Does theory contribute to forecasting accuracy? We use event data to show that a parsimonious model grounded in prominent theories of conflict escalation can forecast civil war onset with high accuracy and over shorter temporal windows than has generally been possible. Our forecasting model draws on “procedural” variables, building on insights from the contentious politics literature. We show that a procedural model outperforms more inductive, atheoretical alternatives and also outperforms models based on countries’ structural characteristics, which previously dominated models of civil war onset. We find that process can substitute for structure over short forecasting windows. We also find a more direct connection between theory and forecasting than is sometimes assumed, though we suggest that future researchers treat the value-added of theory for prediction not as an assumption but rather as a hypothesis to test.

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Blair and Sambanis's test dataset has only 11 instances of civil war onset.
“As an illustration, we examine this issue in detail in the case of Blair and Sambanis86 because their test dataset has a particularly small number of instances of civil war onset (only 11).”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [86]

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Authors: Blair, R. A., & Sambanis, N.
Affiliations: Brown University (US)
University of Pennsylvania (US)
Corresponding author: Blair, Robert A.
ORCID: Blair, Robert A.
Sambanis, Nicholas
Venue: Journal of Conflict Resolution
Publisher: SAGE Publications
Published: 2020-04-24
Volume: 64
Issue: 10
Pages: 1885-1915
ISSN: 0022-0027 (ISSN-L)
1552-8766
DOI: 10.1177/0022002720918923
OpenAlex: W3017867703
Cited by: 52
References: 59
Citations by year: 2026: 8 · 2025: 7 · 2024: 6 · 2023: 10 · 2022: 10 · 2021: 8 · 2019: 2 · 2017: 1
FWCI: 9.34
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Conflict Resolution (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Political Instability Task Force
Topics: Social Sciences › Sociology and Political Science › Political Conflict and Governance
Social Sciences › Sociology and Political Science › Peacebuilding and International Security
Social Sciences › Sociology and Political Science › Qualitative Comparative Analysis Research
Keywords: civil war onset, conflict prediction, procedural variables, contentious politics, structural properties, machine learning, big data, short-term forecasting

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Forecasting Civil Wars: Theory and Structure in an Age of “Big Data” and Machine Learning

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