We present CausalAudit, an open-source Python framework that operationalizes the Partition Stability Index (PSI) methodology introduced by Nguyen, Trần, and Lê (2026) for auditing the robustness of causal graphs in clinical and pharmacological research. The perspectival-relational account of causation (Kriger 2026) holds that causal structure is not observer-independent but jointly produced by the territory's undirected conditional independence skeleton and the bounded observer's variable partition, system boundaries, and temporal ordering. Nguyen et al. (2026) formalized this account by defining a causal compression cost functional and the PSI metric, proving that PSI-maximizing partitions align with the territory's conditional independence structure. However, no ready-to-use software tool implementing these constructs has been available. CausalAudit closes this gap. The framework accepts a tabular clinical dataset and automatically: (1) generates families of perturbed variable partitions via four perturbation operators (coarsening, refinement, rotation, boundary shift); (2) learns the MDL-optimal directed acyclic graph (DAG) for each partition; (3) computes global and per-class PSI using the Structural Intervention Distance; and (4) produces a structured report classifying each edge in the reference causal graph as robust, moderately robust, fragile, or artifact based on its edge robustness score (ERS). The paper describes the six-module architecture (DataIngestor, PartitionPerturber, DAGLearner, GraphDistanceCalculator, PSICalculator, ReportGenerator), introduces a projected SID extension for cross-partition comparison, defines the edge robustness classification schema, and specifies a rigorous evaluation protocol with four testable predictions — without fabricating experimental results. Three clinical use cases are developed: RCT generalizability assessment across populations, pharmacovigilance signal evaluation, and systematic review methodology. This work directly addresses the problem identified by Murthy, Balakrishnan, and Subramaniam (2026) regarding the transferability of RCT-derived causal claims across populations with different coarse-graining structures. Keywords: causal inference; partition stability; PSI; causal audit; MDL; clinical trials; observer-relative causation; Python framework; pharmacological research; compression; bounded observers; DAG; Bayesian networks Related identifiers: References: Kriger, B. (2026). Against Causation. https://doi.org/10.5281/zenodo.18851848 References: Nguyen, D.-M., Trần, T.-L., & Lê, H.-P. (2026). Compression Cost, Partition Stability, and the Engineering of Causal Graphs. Architectures of Inference, 3(2), 117–131. References: Murthy, K., Balakrishnan, A., & Subramaniam, P. (2026). Compression, Causation, and the Sciences. Perspectives in Foundational Science, 4(2), 112–121. License: CC BY 4.0 Resource type: Journal article Publication date: 2026-03-28 Journal: Chulalongkorn Journal of Computational Science and Applied Epistemology, Vol. 12, No. 1, pp. 1–8