About Me:
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Hello - my name is Lan Luo! I am a 5th year Ph.D. candidate in the Quant Marketing division at Columbia Business School. I will be on the 2025-2026 academic job market.
My research enables businesses and researchers to glean insights from unstructured data (like images and text) using causal inference. As an applied methodologist, I develop and leverage cutting-edge methods at the intersection of interpretable machine learning, applied econometrics, and probabilistic machine learning (e.g., deep generative modeling) as they become necessary to addressing marketing problems. I have worked on research in substantive areas pertaining to data-driven design, user-generated content, digital advertising, and social issues such as discrimination and financial decision-making.
I received my B.A. from Yale University, where I double majored in Economics and Statistics & Data Science.
Publications:
Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices
Ryan Dew, Nicolas Padilla, Lan E. Luo, Shin Oblander, Asim Ansari, Khaled Boughanmi, Michael Braun, Fred Feinberg, Jia Liu, Thomas Otter, Longxiu Tian, Yixin Wang, and Mingzhang Yin
International Journal of Research in Marketing, Forthcoming
[ Paper ] [ Preprint ] [ Code Companion ]
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
Anne-Marie Nussberger, Lan Luo, L. Elisa Celis, and Molly J. Crockett
Nature Communications, 2022
[ Paper ] [ Web Appendix ]
Working Papers:
Quantifying Discrimination Based on Facial Femininity: Using Controllable Stimuli Generation for Hypothesis Testing
Lan E. Luo and Olivier Toubia
Under 2nd Round Review at Marketing Science (Major Revision)
[ Preprint ]
Selected Research in Progress:
“*” indicates equal authorship
How Visual Design Drives Success: Interpretable Generative AI for Scalable Hypothesis Generation
Lan E. Luo
To Read or Not To Read: How Individual Review Interactions Influence Search and Purchase
Lan E. Luo*, Sanjana Rosario*, Oded Netzer, and Verena Schoenmueller
Semantic Query Theory: Evidence from Retirement Benefit Claiming
Daniel Russman, Lan E. Luo, Alisa Wu, and Eric J. Johnson
A Timing-based Intervention for Divergent Delivery Bias in Meta Advertising A/B Tests
Lan E. Luo*, Dante Donati*, Oded Netzer*
Improving the Identifiability of Mechanistic Interpretability Methods with Causal Representation Learning
Lan E. Luo, Yixin Wang
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