ABSTRACT
This chapter investigates two cases applying Structural Causal Modeling (SCM) for program evaluation. It examines SCM's effectiveness in identifying causal relationships in social programs, emphasizing its utility for timely decision-making. Two case studies compare an approach to SCM, Precision Analytics (PA), and its application in different intervention contexts – an individual-centered approach and a community-based strategy – illustrating its versatility. The chapter highlights SCM's adaptability across diverse contexts and its rapid data analysis, contrasting traditional methods. It also addresses challenges, including the potential for perpetuating existing inequities, reliance on available data, lack of beneficiary engagement, and limitations of predictive models.
