摘要翻译：
我们提出了一种新的数据建模方法，利用基于经济理论的结构模型作为统计模型的正则化子。我们表明，即使一个结构模型是错误的，只要它是关于数据生成机制的信息，我们的方法可以优于（错误的）结构模型和非结构正则化的统计模型。我们的方法允许将理论作为先验知识进行贝叶斯解释，并可用于统计预测和因果推断。它通过展示如何将理论纳入统计建模可以显著改善域外预测，并为因果效应估计提供了一种综合简化形式和结构方法的方法，从而有助于迁移学习。仿真实验证明了我们的方法在各种情况下的潜力，包括第一价格拍卖，动态的进入和退出模型，以及利用工具变量的需求估计。我们的方法不仅在经济学中有潜在的应用，而且在其他科学学科中也有潜在的应用，这些学科的理论模型提供了重要的洞察力，但受到严重的错误规范的关注。
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英文标题：
《Structural Regularization》
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作者：
Jiaming Mao and Zhesheng Zheng
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  We propose a novel method for modeling data by using structural models based on economic theory as regularizers for statistical models. We show that even if a structural model is misspecified, as long as it is informative about the data-generating mechanism, our method can outperform both the (misspecified) structural model and un-structural-regularized statistical models. Our method permits a Bayesian interpretation of theory as prior knowledge and can be used both for statistical prediction and causal inference. It contributes to transfer learning by showing how incorporating theory into statistical modeling can significantly improve out-of-domain predictions and offers a way to synthesize reduced-form and structural approaches for causal effect estimation. Simulation experiments demonstrate the potential of our method in various settings, including first-price auctions, dynamic models of entry and exit, and demand estimation with instrumental variables. Our method has potential applications not only in economics, but in other scientific disciplines whose theoretical models offer important insight but are subject to significant misspecification concerns.
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PDF链接：
https://arxiv.org/pdf/2004.12601