Large language models (LLMs) have shown impressive capabilities in diverse applications, from complex reasoning to creative generation. However, these models often rely on spurious correlations rather than causal understanding, leading to systematic biases that compromise their fairness and reliability. Current debiasing methods frequently approach bias as a single-dimensional problem, lack frameworks to differentiate between causal relationships and spurious patterns, and typically require extensive model modifications or domain-specific knowledge. We introduce PRISM (Principled Reasoning for Identifying and Suppressing Model Biases), a novel framework that treats bias as a multi-dimensional causal phenomenon and operates through prompt-based learning without model modification. PRISM consists of three core elements: Dimensional Bias Identification (DBI), which isolates distinct causal dimensions of bias; Targeted Example Synthesis (TES), which creates counterfactual examples highlighting specific bias aspects; and Discriminative Learning Enhancement (DLE), which uses these examples to help models distinguish genuine features from spurious correlations. Our comprehensive evaluation across multiple datasets and model architectures demonstrates that PRISM consistently outperforms existing debiasing techniques, particularly for complex, multi-dimensional biases. Additional experiments confirm PRISM’s generalizability across different models and datasets, establishing it as a flexible and effective approach to creating more fair and reliable language models.