Intelligent Information System for Analyzing Success Factors in Public Policies: A Case Study of the Pé-de-Meia Program - Praxedes, Lucas B. F.; A. Filho, Sebastião E. (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de Informação, 2026)
Research Context: Public policies such as the 'Pé-de-Meia' program are crucial for reducing school dropout in Brazil, but their evaluation is complex due to vast amounts of data and inherent selection bias. Scientific and/or Practical Problem: There is a need for an approach that moves beyond simple causal inference to identify the contextual factors that enhance the program's success, providing actionable intelligence for public managers. Proposed Solution and/or Analysis: We developed an intelligent information system that integrates public data and uses a Machine Learning model (LightGBM) to identify the main predictors of success in student retention, complemented by a quasi-experimental analysis (PSM) to estimate impact. Related IS Theory: This work is grounded in the principles of Decision Support Systems (DSS), applying computational intelligence to transform raw governmental data into strategic insights for policy management. Research Method: The methodology involved a four-stage data processing pipeline: data collection (Censo Escolar, Portal da Transparência), feature engineering (creating a proxy for school enrollment dynamics), predictive modeling with LightGBM to rank success factors, and impact estimation using Propensity Score Matching. Summary of Results: The predictive analysis identified that, beyond the natural inertia of previous performance, the number of teachers and program intensity are the most significant actionable predictors of success. To ensure robustness, these factors were cross-validated using a Random Forest model. The quasi-experimental analysis, refined with strict common support and caliper matching, estimated a positive and statistically significant impact (ATT = +0.19 p.p., p-value < 0.001). Contributions and Impact to IS area: This study contributes a novel, rigorously validated method for public policy evaluation within the IS field. It demonstrates how a predictive system can provide immediate strategic insights, while advanced quasi-experimental techniques can detect causal effects even in early-stage implementations, offering a tangible artifact for evidence-based management.