<p>The risk of corporate bankruptcy is increasing with the complex and changing global economic environment, making accurate and reliable bankruptcy prediction models essential. Traditional methods, such as machine learning-based models, often lack semantic clarity, making their results difficult to interpret. Understanding causal relationships is crucial for uncovering the causes and mechanisms of corporate bankruptcy. Causal inference can reveal these relationships, enhancing our understanding of bankruptcy phenomena. Additionally, financial data and market information provided by companies often contain ambiguity and uncertainty. Rough fuzzy set theory, combining rough set and fuzzy set advantages, can handle such data more accurately. This paper proposes a novel multi-attribute prediction decision-making method, CIRF-TOPSIS, which integrates causal inference and rough fuzzy set theory. Unlike existing bankruptcy prediction approaches, CIRF-TOPSIS simultaneously reveals causal mechanisms behind financial indicators and handles fuzziness and uncertainty in data. Empirical testing confirms that CIRF-TOPSIS significantly outperforms traditional MADM methods in both accuracy and interpretability, offering a transparent and reliable decision-support tool for risk management.</p>