题目：Combining Statistical and Structural Models for Improved Prediction and Causal Inference
we propose a set of novel methods for modeling data by combining structural econometric models with (reduced-form) statistical models for improved prediction and causal inference. Our first proposed method is a shrinkage estimator that shrinks a flexible statistical model toward a structural benchmark and permits a Bayesian interpretation of using theory as prior knowledge. Our second proposed method is an ensemble estimator that adaptively combines the two types of models. We show that our methods can outperform both the individual structural model and the (reduced-form) statistical model when they are misspecified. Our papers contribute to transfer learning by showing how incorporating theory into statistical modeling can significantly improve out-of-domain predictions and offer 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. We hope that our methods have potential applications not only in economics, but in other scientific disciplines whose theoretical models offer important insight but are subject to significant misspecification concerns.