Minshen Li

Leading causal inference and AI initiatives across multiple orgs, influencing strategic decisions through work on causal models, advanced analytics, and experimentation.

• Customer In-Stock Value (CIV) Framework: Built CIV Model from scratch; Integrated with Supply Chain Inventory Models; Validated via in-production A/B testing with +12.7% financial lift and no guardrail degradation.
• Causal Impact Evaluation, Incrementality & Forecasting Improvement: Developed causal modeling toolkits (DiD, Synthetic DiD, DML, Causal Forest) for incrementality measurement, heterogeneous treatment effects, and forecasting accuracy improvement.
• Consumer Price Index (CPI) Elasticity Initiative: Developed and operationalized an internal CPI framework to monitor pricing dynamics, estimate demand elasticities, and predict tariff and inflation impacts; integrated into executive dashboards and financial planning to inform forward-looking pricing strategy.
• Promotion Simulators: Built structural econometric models to estimate behavior parameters and simulate counterfactual promotion strategies for optimizing thresholds and discounts.
• AI & Advanced Analytics Innovation: Prototyped multiple AI systems, including LLM-powered analytics agents, digital twins for promotion personalization, and RAG-based clinical AI tools for decision support.

• Ship Fee Simulators: Built and productionized scalable customer demand models to evaluate shipping fee and free-shipping threshold changes across 15+ countries, experimentally validated to inform $116M+ in sales decisions.
• Designed and analyzed 10+ A/B and geo experiments, influencing decisions on pricing, incentives, and product features.

• Coupon Targeting: Developed causal models for coupon response (discount, thresholds) to enhance targeting tools, resulting in 0.6% increase in conversion and 2.1% reduction in promotion cost.
• SKU Substitution Models: Built discrete choice models to capture within-category substitution and deployed tools for optimizing promotion strategy.