The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance
arXiv:2606.17113v1 Announce Type: cross Abstract: Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance. The InferBERT framework integrates transformer models with Do-calculus, but its success hinges on the underlying classification model. This study evaluates the impact of model choice in InferBERT, assessing whether simpler models suffice, if domain-specific pre-training helps, whether scaling to LLMs improves causal detection, and the effect of post-hoc calibration. We performed a comparative study on two benchmarks: Ana
The increasing sophistication of AI models and their integration into critical domains like pharmacovigilance necessitates rigorous evaluation of their reliability and causal inference capabilities.
Improving AI's ability to accurately identify causal adverse drug events has significant implications for patient safety, drug development efficiency, and regulatory oversight.
The focus shifts from merely deploying advanced AI to strategically selecting and calibrating models for dependable causal inference in high-stakes applications.
- · AI developers specializing in causal inference
- · Pharmaceutical companies leveraging AI for safety
- · Regulatory bodies
- · Companies relying on uncalibrated or inappropriate AI models
More reliable detection of adverse drug events from real-world data.
Accelerated drug safety analysis and potentially faster drug approval processes.
Enhanced trust in AI systems for critical medical decisions, expanding their adoption across healthcare.
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Read at arXiv cs.CL