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Chunyu Qu

I am an Economist and Data Scientist at Dun & Bradstreet, where I build analytical systems that turn complex business, infrastructure, and economic signals into decision-ready metrics, forecasts, and causal inference framework.

My work sits at the intersection of AI economics, causal inference, product developments, and public-policy assessment. Recent projects include AI infrastructure metrics governance and decision optimization, Retail Momentum Index (with FedEx), product design and automation, and 2025 LA wildfire labor assessment and planning.

I hold a Ph.D. in Economics from Fordham University and a master's degree in Economics from Duke University.

Economist and Data Scientist focused on AI infrastructure, causal measurement, and decision systems.

Featured Work

AI Infrastructure & Decision Architecture

Auditable metric systems for AI infrastructure utilization, cost allocation, and cross-layer decision governance. Turning research into dashboards, reports, executive briefings, and scalable analytical workflows.

Causal AI & Economic Impact

A/B Test, Experimentation, Quasi-experimental and Causal ML for policy evaluation, firm viability, and program impact.

Retail, labor-market, country-risk, and business-health forecasting using structured data, text signals, and GenAI workflows.

Forecasting & Risk Systems

Frameworks for turning ambiguous AI, product, and policy questions into evaluable metrics, evidence standards, launch-readiness criteria, and repeatable research workflows.

AI Impact, Evaluation & Research Operations