arXiv:2604.14575v2 Announce Type: replace Abstract: Large language models enable inexpensive AI-generated annotations, but using them reliably for causal inference remains challenging. Naively pooling AI and human data induces bias, while existing methods such as Prediction-Powered Inference (PPI; Angelopoulos et al., 2023a) treat AI outputs as proxies of true labels -- an assumption often violated for generative model outputs in practice. We propose Generative Augmented Inference (GAI), a framework that treats AI outputs as general, potentially high-dimensional informative features for learni
The proliferation of powerful generative AI models necessitates a robust framework for integrating their outputs into causal inference without inducing bias, addressing current limitations in methodologies like Prediction-Powered Inference.
This development offers a method to reliably leverage inexpensive AI-generated annotations for scientific and analytical purposes, greatly expanding the scope and efficiency of data analysis.
Researchers can now more confidently use generative AI as a source of informative features rather than just proxies, enabling deeper and more accurate insights from AI-assisted data.
- · AI researchers
- · Data scientists
- · Analytics software providers
- · Industries relying on causal inference
- · Companies relying on outdated causal inference methods
- · Those resisting integration of generative AI
Improved accuracy and efficiency in causal inference across various scientific and business domains due to better AI integration.
Accelerated discovery of complex relationships from large datasets powered by generative AI, leading to new insights and applications.
Enhanced automation of research and analytical tasks, potentially shifting demand for data analysis skills and tools.
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Read at arXiv cs.LG