2020/04/27 by Jiaming Mao, Mao, Jiaming, Zhesheng Zheng +1
Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Econometrics (econ.EM) #Economic Policies and Impacts #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2004.12601
openalex publication_date 2020/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.