The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking

Complex systems and realist evaluation offer promising approaches for evaluat-ing social interventions. These approaches take into account the complex interplay among factors to produce outcomes, instead of attempting to isolate single causes of observed effects. This paper explores the use of Bayesian networks (BNs) in real-ist evaluation of interventions to prevent complex social problems. It draws on the example of the theory-based evaluation of the Work in Freedom Programme (WIF), a large UK-funded anti-trafficking intervention by the International Labour Organi-sation in South Asia. We used BN to explore causal pathways to human traffick-ing using data from 519 Nepalese returnee migrants. The findings suggest that risks of trafficking are mostly determined by migrants’ destination country, how they are recruited and in which sector they work. These findings challenge widely held assumptions about individual-level vulnerability and emphasize that future invest-ments will benefit from approaches that recognise the complexity of an interven-tion’s causal mechanisms in social contexts. BNs are a useful approach for the con-ceptualisation, design and evaluation of complex social interventions