This project is designed to revolutionize general population causal inference by simultaneously accounting for all components of data creation errors while calculating causal associations. This new approach has the potential to advance U-M’s position as the leaders in data collection science as AI increases the breadth of data creation errors we can measure and address. Ultimately, the findings have the potential to drive both improvements in data quality and causal inference.