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[2505.00282] A Unifying Framework for Robust and Efficient Inference with Unstructured Data
Summary
This paper presents a new framework, MAR-S, for robust and efficient inference with unstructured data, addressing biases in neural network predictions and enhancing reproducibility in econometric research.
Why It Matters
As AI technology evolves, the ability to accurately analyze unstructured data is crucial for economists and researchers. This study provides a systematic approach to mitigate biases from neural networks, ensuring more reliable results in econometrics and related fields.
Key Takeaways
- Introduces MAR-S, a framework for unbiased inference with unstructured data.
- Addresses challenges of bias propagation from neural network predictions.
- Connects machine learning methods with traditional econometric problems.
- Develops robust estimators for both descriptive and causal analysis.
- Highlights the importance of reproducibility in research using AI.
Economics > Econometrics arXiv:2505.00282 (econ) [Submitted on 1 May 2025 (v1), last revised 19 Feb 2026 (this version, v3)] Title:A Unifying Framework for Robust and Efficient Inference with Unstructured Data Authors:Jacob Carlson, Melissa Dell View a PDF of the paper titled A Unifying Framework for Robust and Efficient Inference with Unstructured Data, by Jacob Carlson and Melissa Dell View PDF HTML (experimental) Abstract:To analyze unstructured data (text, images, audio, video), economists typically first extract low-dimensional structured features with a neural network. Neural networks do not make generically unbiased predictions, and biases will propagate to estimators that use their predictions. While structured variables extracted from unstructured data have traditionally been treated as proxies - implicitly accepting arbitrary measurement error - this poses various challenges in an era where constantly evolving AI can cheaply extract data. Researcher degrees of freedom (e.g., the choice of neural network architecture, training data or prompts, and numerous implementation details) raise concerns about p-hacking and how to best show robustness, the frequent deprecation of proprietary neural networks complicates reproducibility, and researchers need a principled way to determine how accurate predictions need to be before making costly investments to improve them. To address these challenges, this study develops MAR-S (Missing At Random Structured Data), a semiparamet...