Publications
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2026
Improving Machine Learning Forecasting Explainability and Performance with Causal Feature Selection for Infectious Disease Spread
Brandon Mossop, et al.
INFOR: Information Systems and Operational Research, 2026.
Causal feature selection for forecasting infectious-disease spread, with emphasis on interpretability and predictive performance.
Regime-Aware Causal Bayesian Forecasting for Non-Stationary Time Series
Brandon Mossop, et al.
PLOS ONE.
A regime-aware forecasting method that combines temporal causal discovery, Bayesian structural models, predictive regime classification, and posterior predictive uncertainty.
2025
Agent-guided Causal Discovery with a Small Language Model
Brandon Mossop, et al.
FLLM 2025.
An agent-guided framework combining conditional-independence testing, contextual orientation, edge augmentation, and critique with a small language model.
Working papers and current projects
Agentic Causal Discovery with Small Language Models Through Causal Story Reasoning
Extends agentic causal discovery with explicit causal-story reasoning and evaluation on engineering datasets.
Learning Probabilistic Agent Behavior through Causal Discovery
Explores causal DAG learning and probabilistic structural functions as a data-driven alternative to manually specified agent rules in agent-based models.