Machine learning approaches empirical analysis as algorithms that estimate and compare different alternate scenarios. This fundamentally differs from econometric analysis, where the researcher chooses a specification based on principles and estimates the best model. Instead, machine learning is based on improving and changing models dynamically, which is data-driven. It enables researchers and policymakers to improve their predictions and analysis over time with evolving technology and data points. While research on conflict and violence in economic and policy sciences has progressed substantially in recent times, most of this literature focuses on identifying the possible drivers of conflict while ensuring the underlying assumptions of the data-generating process are met ^[1][2]. Model selection in such an econometric analysis of conflict is dominated by issues of identification, variable direction, as well as the magnitude of estimates, thereby imposing additional constraint on the response of policymakers. Most of the contemporary literature draws a causal connection to conflict with sociopolitical and geographic variables ^[3][4][5][6], whereby researchers identify variables and their associated, unbiased coefficients that cause conflict. The general goal is to maximize the t-statistic, thereby increasing estimation confidence and averting a type II error. However, we ask if from a policy perspective, are these investigations effective in mitigating, and more importantly, preventing conflict? We argue that conflict prevention is better than incurring the costs of conflict. Accordingly, we reformulate the research question to whether a region is susceptible to imminent conflict through a comparison of different econometric and machine learning models.

To examine how binary choice algorithms used in traditional econometrics compares to supervised machine learning algorithms, we have carefully synthesized data on violent conflict, geographical, socioeconomic, political, and agricultural variables. We used data from the Armed Conflict Location and Event Data (ACLED) Project ^[7], which records violence prevalence information, as opposed to the Uppsala Conflict Data Program (UCDP) database, which codes civil war onset and fatality information ^[2][8]. Our rationale for this approach lies in our efforts to impact policy changes at a state or district level. Our approach of including a larger sphere of conflict events, especially less fatal ones, has the potential of generating effective policy pathways to prevent mass atrocities. Conflict prevention or mitigation goes beyond civil war deterrence and the promotion of peace. Often, smaller events are warning signs which, if detected, can prevent potential civil wars. Our combined variables are scaled to a 0.5 × 0.5-degree grid scale. The ACLED data provides information on the prevalence of conflict events including battles, violence against civilians, remote violence, rioting and protesting against a government, and non-violent conflict within the context of war. Next, we have identified and described a set of novel and standard prediction algorithms currently dominating the machine learning literature. Machine learning provides a set of algorithms that can be used to improve prediction accuracy over classic predictive techniques such as logistic algorithm. Supervised (classification) machine learning (ML) approaches have gained popularity due to their ability to outperform simple algorithms such as logistic algorithms, providing more accurate out-of-sample predictions ^[9]. In as much as ML-supervised algorithms have gained popularity, their use in conflict prediction is still limited and class imbalance is not explicitly addressed ^[10][11]. Imbalance occurs when the number of observations reporting conflict are not equally distributed, and this might bias the algorithm’s out-of-sample prediction towards the dominant class (observations or locations without conflict). In our study, we have explicitly addressed classification imbalance. We argue that policymakers can obtain good prediction on incidences of conflict using existing data and applying ML algorithms that can assist in developing the best model for identifying the onset of conflict, thus reducing socioeconomic costs. Finally, we have compared the performances of the logistic model (binary choice model) against four machine learning algorithms and subsequently discuss the best path forward for policymakers.

Contemporary studies on conflict prediction suffer from a few drawbacks, as discussed in detail in the following section. To begin with, most of these studies are limited in their geographical scope or use of high-resolution data. Furthermore, they often indicate low performance in accuracy. Finally, in cases where multiple models are compared, the studies neither explicitly illustrate how addressing imbalance increases model performance nor compare predictive performance between using a reduced set of indicators. In contrast, our initiative distinguishes and contributes to the novel yet emerging body of literature on conflict prediction in the following ways. First, in the realm of the machine learning and conflict mitigation literature, our synthesized datasets are exhaustive and fine-combed. Our study spans over 40 countries in sub-Saharan Africa and examines conflict at a finer grid resolution within each country to predict conflict incidences at a sub-national level. From a policy perspective, our model selection is data driven, whereby a large range of models are considered. However, we have carefully selected confidence intervals as well as document model selection processes. Second, while our intention is neither to carry out causal analysis nor impact evaluation, we have listed the top 25 predictors of conflict that may assist policymakers. We hope that our efforts can at least enable policymakers to focus on a few key drivers of conflict. Third, and most importantly, we have compared performances across five classification algorithms while addressing issues of class imbalance that are pervasive in conflict analysis and provide information on the best imbalance resampling methods that provides the most accurate prediction. Additionally, we compare the differences on the possible efficacy and precision of these models. While the philosophical and scientific needs to investigate the drivers of conflict are undeniable, policy prescription needs to be pragmatic. In our model comparisons, we examine the gains obtained from using different ML models in comparison to the logistic model and discuss how performance metrics of the models may be of different value to different policy markers given the context. The trade-off between the models depends on the objective that the policymaker wants to achieve. Traditional causal analysis of conflict puts emphasis on in-sample prediction power and extreme caution towards not accepting a false hypothesis. However, for successful policies to prevent random yet fatal conflict, out-of-sample predictions along with caution towards neglecting harbingers of catastrophic uprising, i.e., rejecting the true hypotheses, may be more important. ML classification prediction problems are focused on a lower classification error and thereby provide a much better approach. The performance metrics shed light on the important policy conundrum of deciding whether institutions should prioritize preventing conflict or preserving resources for post-conflict development.

2. Research on Conflict Prediction in Sub-Saharan Africa