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Capture-recapture Estimation for Conflict Data and Hierarchical Models for Program Impact Evaluation

A relatively recent increase in the popularity of evidence-based activism has created a higher demand for statisticians to work on human rights and economic development projects. The statistical challenges of revealing patterns of violence in armed conflict require efficient use of the data, and careful consideration of the implications of modeling decisions on estimates. Impact evaluation of a complex economic development project requires a careful consideration of causality and transparency to donors and beneficiaries. In this dissertation, I compare marginal and conditional models for capture recapture, and develop new hierarchical models that accommodate challenges in data from the armed conflict in Colombia, and more generally, in many other capture recapture settings. Additionally, I propose a study design for a non-randomized impact evaluation of the Millennium Villages Project (MVP), to be carried out during my postdoctoral fellowship. The design includes small area estimation of baseline variables, propensity score matching, and hierarchical models for causal inference.

Identiferoai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/12274610
Date07 June 2014
CreatorsMitchell, Shira Arkin
ContributorsCoull, Brent Andrew, Ozonoff, Al
PublisherHarvard University
Source SetsHarvard University
Languageen_US
Detected LanguageEnglish
TypeThesis or Dissertation
Rightsopen

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