Task-Agnostic Machine-Learning-Assisted Inference.

Area of Science:

• Methodology
• Data Science
• Statistical Inference

Background:

• Machine learning (ML) is increasingly vital in scientific research, accelerating discoveries when combined with statistical methods.
• ML-assisted inference, using predictions for downstream analysis, is popular but limited to basic tasks like linear regression.
• Current methods require task-specific derivations, hindering integration with existing statistical software and limiting applications.

Purpose of the Study:

• To introduce a novel statistical framework, PSPS, for task-agnostic ML-assisted inference.
• To enable valid and efficient statistical inference using ML-predicted data across a wide range of analytical tasks.
• To develop a flexible solution that integrates seamlessly with existing statistical software and ML models.

Main Methods:

• Developed PSPS, a novel statistical framework for post-prediction inference.
• Designed PSPS to be task-agnostic, allowing integration with various ML models and statistical routines.
• Ensured inference validity and efficiency, robust to the choice of ML model.

Main Results:

• PSPS provides a post-prediction inference solution adaptable to numerous established data analysis routines.
• The framework supports robust inference, accommodating diverse ML models and statistical approaches.
• Extensive experiments demonstrated PSPS's validity, versatility, and superior performance over existing methods.

Conclusions:

• PSPS significantly advances ML-assisted inference by offering a versatile, task-agnostic solution.
• The framework overcomes limitations of previous methods, enabling broader application of ML-predicted data in statistical inference.
• PSPS facilitates the integration of ML predictions into established statistical workflows, enhancing research efficiency and scope.