New framework enables causal inference with AI-learned representations

A new paper introduces Pragmatic Double Machine Learning (DML), a framework for causal inference when using AI-learned representations as controls. The research demonstrates that cross-fitted DML can provide valid inference for a broad class of estimands, even with imperfect representations. The paper also outlines methods for representation learning and aggregation compatible with DML, and provides techniques for sensitivity analysis when representation errors are substantial. AI

IMPACT Enables more robust causal inference in fields utilizing complex AI-generated data features.

RANK_REASON Academic paper introducing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• AI-Learned Representations
• alphaXiv
• CatalyzeX
• DagsHub
• Double Machine Learning
• Gotit.pub
• Hugging Face
• Pragmatic DML
• ScienceCast
• Wald inference

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