Modern statistical approaches for randomized experiments under interference
- Alex Chin.
- [Stanford, California] : [Stanford University], 2019.
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- This thesis presents new methodology for handling interference in randomized experiments. Interference, a phenomenon in which individuals interact with each other, is widely prevalent in the social and natural sciences, and has major implications for how experiments are optimally designed and analyzed. I first provide an introduction to interference, including examples and a relevant brief history of causal inference. Next, I demonstrate how researchers can use Stein's method to establish limiting distributional results for estimators under interference. The modern tools afforded by Stein's method allow one to analyze certain regimes of arbitrarily dense interference, which goes beyond the analysis capabilities of existing tools. In the subsequent chapter, I develop new model-based, adjustment estimators for estimating the global average treatment effect. The adjustment variables can be constructed from functions of the treatment assignment vector, and the researcher can use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. The final chapter proposes new methods for designing and analyzing stochastic seeding strategies, which are an appealing way of leveraging network structure for marketing, public health, and behavioral interventions. New importance sampling estimators adapted to this setting can greatly improve precision over existing approaches. This thesis is interdisciplinary in nature. Stein's method (Chapter 2), regression adjustments (Chapter 3), and importance sampling (Chapter 4) all command spheres of influence in certain sectors of the literature, and are here repurposed in new domains. I hope that my work shows how existing statistical technology can arise in new arenas of application while simultaneously giving rise to new methodological questions and problems, and in this way, I hope my work is useful for both practitioners and methodologists.
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- Submitted to the Department of Statistics.
- Thesis Ph.D. Stanford University 2019.