Unveiling the Causal Mechanisms Within Multidimensional Poverty

Despite improvements in the design of development interventions from the perspective of the Sustainable Development Goals (SDGs), there is still a lack of evaluation methods able to estimate the impact of these interventions on multiple and interrelated outcomes. This paper proposes a methodological framework for complex causal inference in international development that combines machine learning and econometric designs for causal inference. As a study case, the relationship between multidimensional poverty and violence in Colombia is evaluated following this framework. First, Bayesian networks (BN) are used to create a directed acyclic graph (DAG) able to predict how multidimensional poverty components are interrelated and affected by a violence indicator. Second, the DAG output is used to identify instrumental variables (IV) in order to test the effect of multidimensional poverty on a household’s likelihood to be a victim of violence. Minimum living standards—measured in terms of access to water, connection to the sewage system, and the quality of walls and floors—are strong predictors of the education and health dimensions of poverty. Using 2SLS, the results show that having an illiterate person within a household increases by 0.4% the household’s likelihood to be a victim of violence. BNs have the potential to predict complex causal patterns helping to understand the effect of development interventions on multidimensional outcomes such as poverty. Quasi-experimental econometric designs can then be used to test some of these predicted causal connections.