Causal Inference Guided Defense Mechanisms for LLM-Based Healthcare Decision Systems

Abstract

The integration of large language models into clinical decision support systems promises unprecedented efficiency in diagnosis, treatment planning, and patient management, yet simultaneously introduces severe vulnerabilities to adversarial manipulation and systemic biases. This paper proposes a causal inference framework to guide the design and evaluation of defense mechanisms for large language model based healthcare decision systems. We argue that conventional adversarial robustness techniques, which rely primarily on statistical correlations and input perturbations, are insufficient for high-stakes medical environments where causal structures underlying clinical outcomes must be preserved. By modeling the generative processes that link patient data, clinical reasoning, and decision outputs, causal defense mechanisms can identify and mitigate attacks that exploit spurious correlations while preserving model utility. The paper examines architectural trade-offs between causal shielding, computational overhead, and interpretability, and discusses deployment strategies that integrate causal graph validation, counterfactual reasoning, and structural causal models into the inference pipeline. Governance and policy implications are analyzed in light of regulatory requirements for explainability, fairness, and accountability under frameworks such as the European Union Artificial Intelligence Act and the United States Food and Drug Administration guidelines for software as a medical device. A case illustration is provided using adversarial robustness research on medical decision agents to demonstrate how causal inference can uncover hidden failure modes and inform more resilient system design. The paper concludes by outlining future research directions for sustainable, causally aware large language model infrastructure in healthcare.