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This research's foundation is the development of innovative statistical machine learning methods that enable organizations to utilize the massive quantities of diverse data they generate for data-driven decision making and knowledge discovery. It has especially focused on the development and deployment of accurate and computationally efficient methods for anomalous pattern detection, which is motivated by the realization that many real-world problems faced by social science can be reduced to the tasks of detecting patterns of unexpected outcomes. At its core, this research seeks to bridge the gap between machine learning and the social sciences: economics, management, and public policy, both through the application of machine learning methods to social science problems and through the integration of machine learning and econometric methodological approaches.