ABSTRACT
ABSTRACT To better understand and describe the world around them, social scientists need to be able to create, analyze, and validate social, political, and economic models that include causal and predictive elements. Causality is, however, notoriously difcult to analyze. To address this complexity, we describe a suite of causal/predictive analysis techniques adapted from a variety of social, natural, and computational science applications, specically chosen for their unique applicability to the problems of analyzing complex causes and effects in social science and demonstrate how these methods can be used to understand the causal relationship between poverty and conict.
