<ul data-eligibleForWebStory="false"><li>This paper introduces a framework for conducting efficient inference on parameters derived from unstructured data like text, images, audio, and video.</li><li>The framework addresses the challenges of bias in predictions made by neural networks and the downstream estimators that rely on structured data extracted from unstructured inputs.</li><li>By reframing inference with unstructured data as a missing structured data problem, the framework applies classic results from semiparametric inference to create valid, efficient, and robust estimators.</li><li>The framework, known as MAR-S, provides economists with tools to construct unbiased estimators using unstructured data and is demonstrated through the re-analysis of influential studies.</li></ul>

A Unifying Framework for Robust and Efficient Inference with Unstructured Data

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