Causal AI-AugETM: Integrating Structural Causal Models with Exposure–Toxicity Modeling for Robust Drug Safety Assessment Under Confounding Bias
Keywords:
causal inference, structural causal models, exposure-toxicity modeling, drug safety, confounding bias, AI-augmented clinical trials, robust assessment, socio-technical infrastructure, fairness, governanceAbstract
The assessment of drug safety in early-phase clinical trials and post-market surveillance is persistently challenged by confounding bias arising from non-randomized treatment assignment, time-varying exposures, and unmeasured covariates. Traditional exposure-toxicity joint models, while effective in capturing dose-response relationships, lack the structural causal reasoning necessary to distinguish genuine toxicity signals from spurious associations. This paper introduces Causal AI-AugETM, a comprehensive framework that integrates structural causal models with an AI-augmented exposure-toxicity joint modeling paradigm. The framework systematically embeds causal graphs, intervention calculus, and counterfactual reasoning into the exposure-toxicity modeling pipeline, enabling robust estimation of causal effects under complex confounding scenarios. Emphasis is placed on the system-level architecture that governs data ingestion, causal discovery, model inference, and feedback loops for continuous updating. The design explicitly addresses structural trade-offs between model flexibility and interpretability, computational feasibility and statistical precision, as well as fairness across demographic subgroups that may be differentially affected by confounding by indication. Furthermore, the paper discusses governance and policy implications, including regulatory validation standards, transparency requirements, and accountability mechanisms for AI-driven safety assessments. Deployment considerations such as scalability via cloud-native and federated infrastructures, sustainability of model retraining cycles, and integration with existing pharmacovigilance systems are examined. Cross-domain comparisons with causal inference applications in climate science and econometrics provide additional insight into best practices. The Causal AI-AugETM framework represents a principled step toward trustworthy, causally informed drug safety assessment that can adapt to evolving real-world data environments while maintaining scientific rigor and societal trust.
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