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

Rogerio de Avila

No z7der_v1, Thesis Commons from Center for Open Science

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.

Date: 2024-11-07
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/672d0c51ff2bca1fa64d6caa/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:thesis:z7der_v1

DOI: 10.31219/osf.io/z7der_v1

Access Statistics for this paper

More papers in Thesis Commons from Center for Open Science
Bibliographic data for series maintained by OSF ().