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Utilizing Big Administrative Data in Evaluation Research: Integrating Causal Modeling, Program Theory, and Machine Learning

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  • de Avila, Rogerio

Abstract

The increased availability of administrative data and big data, coupled with advances in causal modeling and data analytics, presents new opportunities to enhance program evaluation in public policy and social sciences. This thesis investigates how these modern theory-driven approaches can be integrated with traditional methodologies to address complex causal questions, enhancing evaluations' effectiveness, timeliness, and comprehensiveness. Guided by substantial theoretical frameworks such as those proposed by Funnell and Rogers (2011) and empirical studies like Pearl (2009), this research addresses gaps in data utilization, ethical standards, and the application of machine learning. Specific challenges include improving the precision and comprehensiveness of data analysis, ensuring ethical data use as advocated by frameworks like the Five Safes, and enhancing interdisciplinary collaboration and training. This thesis aims to demonstrate significant advancements in program evaluation by bridging these gaps, proposing a paradigm shift towards a more integrated and data-informed approach in public policy and social sciences.

Suggested Citation

  • de Avila, Rogerio, 2024. "Utilizing Big Administrative Data in Evaluation Research: Integrating Causal Modeling, Program Theory, and Machine Learning," Thesis Commons z7der_v1, Center for Open Science.
    • Handle: RePEc:osf:thesis:z7der_v1
    DOI: 10.31219/osf.io/z7der_v1
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    References listed on IDEAS

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    1. Tanvi Desai & Felix Ritchie & Richard Welpton, 2016. "Five Safes: designing data access for research," Working Papers 20161601, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    2. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
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