A Unifying Framework for Robust and Efficient Inference with Unstructured Data
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非结构化数据稳健与有效推断的统一框架
- 研究问题
- 经济研究使用神经网络从文本、图像等资料提取变量时，如何纠正预测误差，并应对模型选择与更新带来的推断问题？
- 方法与思路
- 作者提出 MAR-S 半参数缺失数据框架，利用验证样本纠正神经网络预测误差，并处理预测值的聚合与变换。
- 材料陈述的贡献
- 摘要称，框架整合并扩展基于机器学习预测的去偏推断，为描述性与因果目标量构造无偏、有效且稳健的估计方法。
- 阅读关注点
- 应核对验证样本的抽样与缺失机制、真实变量的验证方式，以及预测误差修正和聚合变换后的有效性条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未列出缺失机制和验证条件，不能据此认定任意预测偏差均可消除。
原文摘要
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 semiparametric missing data framework that enables unbiased, efficient, and robust inference with unstructured data, by correcting for neural network prediction error with a validation sample. MAR-S synthesizes and extends existing methods for debiased inference using machine learning predictions and connects them to familiar problems such as causal inference, highlighting valuable parallels. We develop robust and efficient estimators for both descriptive and causal estimands and address inference with aggregated and transformed neural network predictions, a common scenario outside the existing literature.