This paper presents CounterScene, a novel framework that enables structured counterfactual reasoning in generative Bird's Eye View (BEV) world models for safety-critical driving scenario generation. The approach addresses the challenge of creating realistic yet adversarially effective safety scenarios by introducing causal adversarial agent identification to determine critical agents and conflict types. CounterScene develops a conflict-aware interactive world model using causal interaction graphs to explicitly model dynamic inter-agent dependencies. The framework performs minimal interventions through stage-adaptive counterfactual guidance, effectively bridging the gap between realism and adversarial robustness in autonomous driving safety evaluation.