Publications
Peer-reviewed and ongoing research on electricity markets, causal machine learning, forecasting, and infrastructure resilience.
Under Review
Explainable Machine Learning Analysis of Retail Renewable Premiums
Yufan Ji, Noah Dormady, Abdollah Shafieezadeh, others
Under review 2026
A matched-offer and explainable machine learning study of renewable electricity premiums across utilities, time periods, and contract attributes.
Causal Graph-Informed Temporal Convolution for Electricity Price Forecasting
Yufan Ji, Abdollah Shafieezadeh, Noah Dormady
Under review 2026
A causal graph-informed temporal convolutional network for accurate and interpretable retail electricity price forecasting.
Published

Robust Treatment Assignment with Uncertainty-Aware Causal Forests: Joint Optimization of Accuracy and Estimation Uncertainty
Yufan Ji, Abdollah Shafieezadeh
Expert Systems with Applications 2026
An uncertainty-aware causal forest framework that jointly considers predictive accuracy and confidence-interval width to support stable, calibrated, and risk-aware treatment assignment.

Efficiency and Consumer Welfare Under Retail Electricity Deregulation: Analysis of Ohio's Retail Choice Markets
Noah Dormady, William Welch, Yufan Ji, Stephanie Pedron, Abdollah Shafieezadeh, Alberto Lamadrid, Matthew Hoyt, Samantha Fox
Journal of Critical Infrastructure Policy 2025
A consumer-centered analysis of daily Ohio retail electricity offers showing that many competitive offers exceeded default utility prices and that welfare-improving choices were inconsistently available.