Human Rights Treaty Commitment and Compliance: A Machine Learning-Based Causal Inference Approach
Why do states ratify international human rights treaties? How much do human rights treaties influence state behaviors directly and indirectly? Why are some human rights treaty monitoring procedures more effective than others? What are the most predictively and causally important factors that can reduce and prevent state repression and human rights violations? This dissertation provide answers to these keys causal questions in political science research, using a novel approach that combines machine learning and the structural causal model framework. The four research questions are arranged in a chronological order that reflects the causal process relating to international human rights treaties, going from (a) the causal determinants of treaty ratification to (b) the causal mechanisms of human rights treaties to (c) the causal effects of human rights treaty monitoring procedures to (d) other factors that causally influence human rights violations. Chapter 1 identifies the research traditions within which this dissertation is located, offers an overview of the methodological advances that enable this research, specifies the research questions, and previews the findings. Chapters 2, 3, 4, and 5 present in chronological order four empirical studies that answer these four research questions. Finally, Chapter 6 summarizes the substantive findings, suggests some other research questions that could be similarly investigated, and recaps the methodological approach and the contributions of the dissertation.
Clark, Purdue University.
International Relations|Political science
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