Mining for Causal Relationships: A Data-Driven Study of the Islamic State
This work addresses the need for data-driven insights into insurgent group dynamics for security analysts and policymakers, though it is incremental in applying existing methods to a new dataset.
The paper tackles the problem of understanding causal relationships in ISIS military activities by analyzing 2200 incidents, resulting in the identification of association rules linking various types of attacks and operations, such as VBIED activity in Syria with military actions in Iraq and coalition air strikes.
The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.