In Evaluation review
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.
Grueso Hernando
2022-Nov-24
Bayesian networks, complex causality, impact evaluation, instrumental variable, multidimensional poverty, violence
