Felicia Nguyen

Ph.D. Candidate, Quantitative Marketing
Emory University
AI & Digital Platforms • Causal Inference • Marketplace Design

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Atlanta, GA · fnguyen.netlify.app · GitHub · LinkedIn

Hiya, I’m Felicia 😸

I am a Ph.D. candidate in Quantitative Marketing at Emory University. I study AI and digital platforms, online communities, and algorithmic governance using quasi-experiments and scalable measurement from multimodal data.

Research interests: AI & digital platforms; online communities; information design; digital marketing; responsible AI.
Methods: Causal Inference - DiD/RD/IV, experiments, debiased ML, deep learning, foundation models.

Education

Emory University
Ph.D. Candidate, Quantitative Marketing (Expected 2026)
Advisor: David Schweidel

University of Wisconsin–Madison
MBA & M.S., Business Analytics (2020)

Foreign Trade University
B.S., Economics (2015)

Selected papers

Job Market Paper

Variety and Uniqueness in Online Content: A Multimodal Representation Learning Approach

Phuc Hung Nguyen · David Schweidel

Under review

We develop a multimodal representation-learning pipeline to measure content strategy at scale using 5M Instagram posts. Engagement exhibits an inverted-U relationship with diversification, and differentiation benefits smaller creators most. An instrumental-variables design supports a causal interpretation.

Working papers

Selling Certainty, Buying Entry: The Causal Effect of a “No Reserve” Label in Online Auctions (R&R at Management Science)
Beyond Likes: The Impact of Engagement Signal Obfuscation (Working paper)
Moderation Strategy and Expertise Signaling in an Online Health Consultation Community (Working paper)

Publications & proceedings

Nguyen, An ; Nguyen, Phuc Hung. “Innovation Cascades: Amazon Headquarters 2’s Impact on Local Innovation Activities.” PACIS Proceedings (2024). Details

Latest posts

CUPED and CUPAC in Practice: Variance Reduction for A/B Tests (with R Code)

A practical guide to CUPED and CUPAC, with intuition, pitfalls, and an end-to-end R implementation you can reuse.

Feb 1, 2026

Anytime-valid inference for continuous monitoring: confidence sequences in R

A practical tutorial for A/B tests you want to monitor continuously, with simulations and the Hillstrom email marketing dataset.

Jan 6, 2026

Proximal Causal Inference: A Practical Guide (with R Code)

How to estimate causal effects with unmeasured confounding using proxy variables, with a worked simulation and a real-data walkthrough in R.

Nov 18, 2025

All posts →

Current status

Expected graduation: 2026
Primary affiliation: Emory University (Goizueta Business School)
Open to: research collaborations and data-driven roles in experimentation/causal inference, marketplaces, and platform product analytics

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