Moving Beyond Correlation with Causal AI
When a batch deviates from expectations, how confident are you that you’ve identified the parameter responsible? A strong correlation in your CPV data may point to a potential cause, but it can also lead you in the wrong direction. Differences in raw materials, equipment or operating conditions can obscure the real drivers of product quality. Is there sufficient evidence to support a root-cause conclusion, justify a CAPA or approve a process change?
Key Takeaways:
In this webinar, we’ll explore how Causal AI can help make better use of existing batch data to investigate the relationships between process parameters and CQAs. Through a demonstration of a Causal AI Agent, we’ll show how process knowledge and statistical evidence can be brought together to identify potential causal drivers, challenge existing assumptions and recognize when the available data is insufficient to support a conclusion. We’ll also discuss how these insights can support deviation investigations, CAPA effectiveness and continued process verification in a GxP environment.
