论文标题
通过统计和结构模型的合奏学习
Ensemble Learning with Statistical and Structural Models
论文作者
论文摘要
统计和结构建模代表了两种不同的数据分析方法。在本文中，我们提出了一组新的方法，用于结合统计和结构模型，以改善预测和因果推断。我们第一个提出的估计器具有双重鲁棒性属性，因为它仅需要正确规范统计模型或结构模型。我们的第二个提议的估计器是一个加权的集合，它在两个模型都被弄清楚时具有胜过两个模型的能力。实验证明了我们在各种设置中的估计器的潜力，包括固定价格的拍卖，进入和退出的动态模型以及使用工具变量的需求估计。
Statistical and structural modeling represent two distinct approaches to data analysis. In this paper, we propose a set of novel methods for combining statistical and structural models for improved prediction and causal inference. Our first proposed estimator has the doubly robustness property in that it only requires the correct specification of either the statistical or the structural model. Our second proposed estimator is a weighted ensemble that has the ability to outperform both models when they are both misspecified. Experiments demonstrate the potential of our estimators in various settings, including fist-price auctions, dynamic models of entry and exit, and demand estimation with instrumental variables.
