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Transnational Public-Private Governance Initiatives (TGIs) in which states and intergovernmental organizations cooperate with business and non-governmental organizations are an important element of contemporary world politics. Despite the proliferation of TGIs as instruments of global governance, we know little about their effectiveness. Existing works are largely based on single or small-n case studies. Their findings are difficult to compare and potentially suffer from selection bias resulting from a focus on successful initiatives and specific issue areas. In this paper, I develop a machine learning approach to analyzing the effectiveness of a large number of TGIs operating in a broad range of issue areas. Focusing on 570 TGIs operative in 2017 and using methods of automated data collection and text analysis, I map the outputs of TGIs and how they match with the governance functions they perform. I also explore the factors that explain variation in TGI effectiveness using statistical analysis.