Wildfire risk associated with electric power infrastructure has emerged as a critical safety, reliability, and
environmental challenge, particularly in regions experiencing increasingly severe fire weather conditions. Effective
risk management requires modeling the complex chain of events linking environmental conditions, operational
decisions, infrastructure failures, fire ignition, wildfire spread, emergency response, and societal consequences. This
paper presents a comprehensive wildfire probabilistic risk assessment (PRA) methodology for power utilities based
on a Hybrid Causal Logic (HCL) framework. The framework models wildfire risk as a sequence of probabilistic
events beginning with meteorological triggers and operational decisions such as proactive system de-energization,
followed by asset failure and ignition mechanisms, wildfire spread dynamics, suppression effectiveness, and
evacuation outcomes. Each stage is quantified using appropriate modeling approaches, including reliability models
for infrastructure failures, statistical or machine learning models for ignition probability, physics-informed fire
spread simulations for consequence estimation, and probabilistic evacuation models for life-safety analysis. By
linking these modules within a unified causal structure, the HCL methodology enables consistent propagation of
uncertainties and interdependencies across the wildfire risk chain. The framework produces multi-dimensional risk
metrics and probability–consequence curves representing safety, reliability, financial, and environmental impacts.
These outputs can be aggregated from component-level events to power line-level and system-level risk metrics,
supporting risk-informed decision making across operational, tactical, and strategic planning horizons. The
methodology is designed to support key utility applications, including Public Safety Power Shutoff (PSPS) decision
support, wildfire mitigation prioritization, system hardening and undergrounding investment planning, and
regulatory risk-based compliance frameworks. The method has been implemented in a comprehend software
platform for use by power utility companies.