To assess the consequences of armed conflict, especially warfare, one must compare what happened to the counter-factual. In a quasi-experimental sense, a given country's synthetic without conflict is compared to the country's
actual experience with warfare. Preliminary analysis using simulations and fixed-effects regression methods provide new insights into the economic and development consequences of armed conflict. Data issues, however, cloud the picture. Endogeneity and omitted variable bias are significant problems. Fixed Effect regressions cannot address endogeneity problems, but do handle omitted variables. A quasi-experimental approach is needed to address these problems. Nevertheless, the application of the synthetic control method is not straightforward. Two problems are especially troublesome for examining how armed conflict affects the SDGs. The first has to do with the problem that conflicts then to take place in different time periods. This creates a problem for the comparison of the countries that have experienced armed civil conflict, since they have experienced conflict at different points of time. The second problem is that the world has made significant progress in improving the living condition of many through improvements on several SDGs. Infant mortality and malnutrition are falling fast across all countries. It is difficult to ascertain given these generic trends what effect armed conflict has on these SDGs. This paper explores the methodological and data challenges to assessing the effect of armed conflict on the sustainable development goals and the application of Synthetic Control Methods (SCM).