Post Prediction Inference

Post-prediction inference (PPI) addresses the challenges of using machine learning (ML)-predicted outcomes in subsequent statistical analyses. Current research focuses on developing robust and efficient PPI methods that account for the uncertainty introduced by ML prediction, often employing techniques like negative control outcomes or weighted Z-estimation to ensure valid inference, even with complex models and adaptive data collection strategies. This work is crucial for leveraging the power of ML in scientific research while maintaining the rigor of statistical inference, enabling more reliable conclusions from increasingly prevalent data integration and prediction tasks. The resulting improvements in accuracy and efficiency have broad implications across diverse scientific fields.