Lan E. Luo
Assistant Professor of Marketing, Yale School of Management
Hello - my name is Lan Luo! I am an Assistant Professor of Marketing at the Yale School of Management. I received my Ph.D. from the Quantitative Marketing division at Columbia Business School.
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
-
Discrimination Against Femininity in Headshots: A Field Experiment with AI-Enabled Controllable Stimuli Generation
Marketing Science, Forthcoming
Abstract
We document an understudied form of discrimination based on femininity expressed in headshots. To do so, we use a Generative Adversarial Network to create realistic headshots of people and then manipulate their femininity independently of other attributes like pose, general facial expression, background, hairstyle, and clothes. Then, we devise an experimental design that allows us to identify the separate and combined causal impact of femininity and gender identity (proxied by gender pronouns) on real-life outcomes. In a field experiment within a naturalistic advertising environment, we find that prospective customers of an education company discriminated against femininity at various stages of the purchase funnel, to a largely similar extent for men, women, and non-binary people. The findings inform managers and policymakers of an important form of discrimination that would otherwise be underestimated by disregarding headshots. Methodologically, we introduce a novel framework for testing specific hypotheses pertaining to causal effects of treatments which are derived from unstructured data. The approach involves using natural text to readily identify hypothesis-relevant features from a generative AI model (in a particularly disentangled and interpretable representation space) and then creating realistic stimuli that vary controllably in those features.
-
Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices
International Journal of Research in Marketing, 2026
Abstract
Making sense of massive, individual-level data is challenging: marketing researchers and analysts need flexible models that can accommodate rich patterns of heterogeneity and dynamics, work with and link diverse data types, and scale to modern data sizes. Practitioners also need tools that can quantify uncertainty in models and predictions of consumer behavior to inform optimal decision-making. In this paper, we demonstrate the promise of probabilistic machine learning (PML), which refers to the pairing of probabilistic modeling and machine learning methods, in pushing the frontier of combining flexibility, scalability, interpretability, and uncertainty quantification for building better models of consumers and their choices. Specifically, we overview both PML models and inference methods, and highlight their utility for addressing four common classes of marketing problems: (1) uncovering heterogeneity, (2) flexibly modeling nonlinearities and dynamics, (3) handling high-dimensional and unstructured data, and (4) addressing missingness, often via data fusion. We also discuss promising directions in enriching marketing models, reflecting recent developments in representation learning, causal inference, experimentation and decision-making, and theory-based behavioral modeling.
-
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
Nature Communications, 2022
Abstract
As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate robust and positive attitudes towards interpretable AI among non-experts that generalize across a variety of real-world applications and follow predictable patterns. Participants value interpretability positively across different levels of AI autonomy and accuracy, and rate interpretability as more important for AI decisions involving high stakes and scarce resources. Crucially, when AI interpretability trades off against AI accuracy, participants prioritize accuracy over interpretability under the same conditions driving positive attitudes towards interpretability in the first place: amidst high stakes and scarce resources. These attitudes could drive a proliferation of AI systems making high-impact ethical decisions that are difficult to explain and understand.
Working Papers
-
How Visual Designs Drive Success: Interpretable Generative AI for Data-Driven Design
Draft available upon request
Abstract
Visual designs are often used in marketing (e.g., packaging, ads, media covers) to achieve a variety of business outcomes, like improved sales, click-through rates, and brand attitudes. Since designs are complex, unstructured data, it is difficult to determine what features drive their success in a way that is interpretable and managerially actionable. To address this challenge, I develop a novel methodological framework to automatically discover what interpretable features make visual designs in a given domain successful. I first leverage a deep generative text-to-image AI model (by fine-tuning Stable Diffusion 3.5 in my application) that adopts the role of designer and enables visual designs to be described by low-dimensional design representations. Then, I apply a novel adaptation of cutting-edge “mechanistic interpretability” methods—specifically “sparse autoencoders” typically applied to large language models—to scalably discover a taxonomy of interpretable and managerially relevant features predictive of success from these design representations. Finally, I generate image redesigns by manipulating features of interest to help managers scalably pilot data-driven design changes.
I apply this framework to discover how book cover redesigns predict sales on Amazon.com using a unique dataset I collected of over 160,000 books. I discover a diverse set of interpretable features related to illustration, typography, composition, and layout. I then create realistic cover redesigns predicted to improve sales by manipulating those features (e.g., redesigns with lower contrast and less separation of text and graphical elements). In a holdout analysis with a rich set of control variables, including just 30 of these discovered features (out of 9,728) improves variation explained in sales by nearly as much as prices and by more than reviews. Back-of-the-envelope calculations suggest that a large publisher could leverage this subset of features to increase annual revenue for the whole publisher by over $9.1 million, reflecting a change in sales equivalent to introducing an 8.5% price discount. In a lab study, I find causal evidence that the proposed methodological framework can redesign covers to significantly improve preferences, and that generative AI can help level the playing field in the publishing industry.
Selected Research in Progress
“*” indicates equal authorship.
- Theory-Guided AI Conversations as Flexible Choice Architecture: Evidence from Retirement Benefit Claiming
- Sparse Autoencoders Reveal (and Generate) the Drivers of Online Sharing
- How Livestreaming Chats Engage Consumers
- Causality in Conversations
- AI-Powered Conjoint Analysis