From Treatment to Outcome: Advanced Causal Inference and Machine Learning for Resource Evaluation

This thesis introduces a comprehensive framework that synergistically integrates causal inference with machine learning to enhance the analysis of treatment effects and optimize resource allocation strategies, particularly from a policymaker’s perspective. The framework addresses a critical gap at the intersection of predictive modeling and causal analysis by overcoming the challenges posed by the absence of counterfactual scenarios in historical data—an issue that often limits the accuracy and reliability of traditional approaches. The framework is organized into two primary stages. In the first stage, advanced machine learning models are employed to establish robust pre-treatment baselines, accurately predicting outcomes and identifying observations at risk of adverse outcomes. This enables more precise targeting of interventions by focusing resources on individuals or subpopulations that are most likely to benefit from them. By doing so, the framework not only improves the efficiency of interventions but also reduces the risk of wasted resources on low-impact efforts. In the second stage, causal inference techniques are applied to evaluate the effectiveness of these interventions. This stage utilizes post-treatment data to assess both immediate and long-term outcomes, providing a comprehensive understanding of the intervention’s impact. A key component of this stage is the reassessment of outcomes using machine learning models, which allows for continuous monitoring and adjustment of strategies. Furthermore, in scenarios where resources are constrained, the framework includes a ranking system based on the effectiveness of the treatment. This ranking system prioritizes interventions for those who are most likely to experience significant benefits, ensuring that resources are allocated in the most efficient manner possible. By integrating these stages, the framework provides a dynamic, iterative process that continuously refines intervention strategies over time. This approach offers policymakers and practitioners a powerful tool for data-driven decision-making, enabling them to respond to evolving conditions and emerging data with precision and flexibility. The effectiveness of the proposed framework is demonstrated through detailed case studies, including an analysis of synthetic datasets, which allows for controlled experimentation and validation. Additionally, a real-world application within an educational context is explored, focusing specifically on the impact of financial aid interventions on student outcomes. These case studies underscore the framework’s potential to significantly enhance the precision and effectiveness of intervention strategies, leading to better resource allocation and, ultimately, more favorable outcomes. Beyond its practical applications, this thesis contributes to the broader field of causal analysis by offering a scalable and adaptable methodology that can be applied across various domains. The framework’s flexibility allows it to be tailored to different contexts, making it a valuable tool for addressing complex challenges in fields such as healthcare, social policy, and economics. The concluding discussion reflects on the broader implications of these findings for policy and practice, and outlines several avenues for future research, including the exploration of more advanced causal inference techniques and the potential integration of real-time data to further enhance decision-making processes.