About Me:
Hello - my name is Lan Luo! I am a 5th year Ph.D. candidate in the Quant Marketing division at Columbia Business School.
My research focuses on gleaning business insights from unstructured data like images and text using causal inference. Recently, my work explores how to estimate treatment effects in the contexts of book covers, user-generated content, and social issues like discrimination. This research lies at the nexus of several methods including deep generative modeling (e.g., text-to-image synthesis and large language models), interpretable machine learning, and applied econometrics.
I received my B.A. from Yale University, where I double majored in Economics and Statistics & Data Science.
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 ]
Quantifying Discrimination Based on Facial Femininity: Using Controllable Stimuli Generation for Hypothesis Testing
Lan E. Luo and Olivier Toubia
Major Revision at Marketing Science: Frontiers
[ Preprint ]
How Do Book Covers Drive Sales? Scalable Hypothesis Generation Using Interpretable Multimodal Generative AI
Lan E. Luo
Stars in the Text: The Impact of Review Inconsistencies on Purchase Likelihood
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