Shin Oblander
Shin Oblander is a quantitative marketing researcher who conducts both methodological and substantive research. Methodologically, their research focuses on developing tools for problems such as causal inference, data fusion, and analysis of unstructured data using econometric and probabilistic machine learning approaches. Substantively, their research spans topics such as the implications of subscription-based business models and how people make decisions in complex and uncertain environments. Shin’s research has been published in leading academic journals including Marketing Science, Psychological Science, and Manufacturing & Service Operations Management. Their work has been featured in media outlets such as The Wall Street Journal and New York Magazine. They are also a faculty member of UBC's Centre for AI Decision-making and Action (CAIDA) and Rethinking the Right-of-Way (ReROW) research clusters. Outside of work, Shin enjoys public transit advocacy, trainspotting, genderless fashion, weightlifting, and rhythm games.
Selected Publications
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Daniel Minh McCarthy, Shin Oblander, Young-Hoon Park, and Yeohong Yoon (2026). “Expanding Markets or Capturing Share? The Competitive Effects of Subscriptions on Spending in Restaurant Delivery.” Journal of Marketing Research, forthcoming.
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Shin Oblander and Kinshuk Jerath (2026). "The Non-Linear Impact of Promised Delivery Time on Online Purchasing." Manufacturing & Service Operations Management, 28(4), 1051-1067.
- Michal Herzenstein, Sanjana Rosario∗, Shin Oblander, and Oded Netzer∗ (2024). “The Language of (Non)replicable Social Science.” Psychological Science, 35(9), 1048-1061.
- Shin Oblander and Daniel Minh McCarthy (2023). “Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19.” Marketing Science, 42(5), 839-852.
- Daniel Minh McCarthy, and Shin Oblander (2021). “Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis.” Marketing Science, 40(3), 459-480.