Wildfire risk in power grids is driven by complex interactions among meteorological conditions, fuel characteristics, asset health, vegetation exposure, operational decisions, fire spread behavior, and community vulnerability. Traditional wildfire risk approaches often rely on deterministic hazard maps or isolated probability models, which may not fully capture the sequential and interdependent nature of wildfire ignition, propagation, and consequence development. This paper presents an integrated probabilistic risk assessment framework for wildfire risk in electric power systems using Hybrid Causal Logic. The proposed framework combines event sequence diagrams, fault trees, and Bayesian networks to model wildfire scenarios from initiating conditions through ignition, spread, evacuation effectiveness, and final consequences. The methodology supports the integration of asset failure models, vegetation ignition models, meteorological triggers, Public Safety Power Shutoff decisions, fire spread simulations, and consequence metrics including safety, reliability, financial, and environmental impacts. The framework also enables aggregation of risk from component and circuit levels to broader system and service-territory levels. Risk outputs are represented through probability distributions, exceedance curves, expected consequence values, and heat-map indicators to support risk-informed prioritization of mitigation strategies such as grid hardening, vegetation management, sectionalization, and proactive de-energization. The proposed approach provides a flexible and scalable foundation for planning, operational monitoring, and real-time wildfire decision support in power utilities.