AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach
This paper proposes an assumption-lean partial identification framework that leverages AI-generated measurements as "weak shadow variables" to construct sharp bounds and perform inference on population quantities under missing-not-at-random conditions, demonstrating significant improvements in estimation accuracy and interval precision over classical methods in semi-synthetic experiments.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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