As artificial intelligence systems become increasingly autonomous and deployed in complex multi-agent environments, the need for robust governance mechanisms that can adapt to novel situations becomes critical. This paper introduces a novel framework for causal, self-governing AI agents that leverages counterfactual reasoning to develop emergent norms within multi-agent systems. Our approach combines causal inference models with distributed governance protocols, enabling agents to reason about the consequences of their actions, learn from hypothetical scenarios, and collectively establish behavioral norms without centralized control. I propose a three-tier architecture: (1) a causal reasoning engine that constructs and maintains causal models of the environment and other agents, (2) a counterfactual inference module that generates and evaluates alternative action sequences, and (3) a norm emergence protocol that facilitates the collective development of behavioral guidelines through agent interactions. Through theoretical analysis and simulation experiments, I demonstrate that this framework enables agents to develop coherent, adaptive governance structures that improve system-wide outcomes while maintaining individual agent autonomy. My results show that causal self-governing agents achieve superior performance in complex coordination tasks, exhibit more robust behavior under distribution shifts, and develop interpretable governance structures that align with human values. This work contributes to the growing field of AI safety and governance by providing a principled approach to autonomous agent coordination that scales with system complexity.