Publications

Selected publications and research outputs.

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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.

Code

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.

Project Code

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.

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