基于图学习的工具变量检测：应用于房屋定价的高维GIS-普查数据
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing
摘要：
内变量偏差和工具变量验证一直是统计学和计量经济学研究的重要课题。在大数据时代，这些问题通常与维度问题相结合，因此需要更多的关注。在本文中，我们合并了两个著名的工具，从机器学习和生物统计学--变量选择算法和概率图--估计房价和相应的因果结构，使用2010年悉尼的数据。估计使用了一个200千兆字节的三维数据库，其中包括当地学校数据、地理信息系统信息、人口普查数据、房屋特征和其他社会经济记录。使用“大数据”，我们表明，它是可能的，以有效地执行数据驱动的仪器选择和清除无效的仪器。我们的方法提高了稀疏的变量选择，稳定性和鲁棒性存在的高维，复杂的因果结构和随之而来的多重共线性，并恢复稀疏和直观的因果结构。该方法在内模检测、仪器验证、弱仪器剪枝和有效仪器选择等方面具有较高的效率和有效性。从机器学习的角度来看，估计结果既符合又证实了悉尼住宅市场的实际情况、经典的经济理论和前人的联立方程模型的研究结果。此外，估计结果与经典的计量经济学工具，如两阶段最小二乘回归和不同的仪器测试是一致的，并支持。所有代码可在\url{https://github.com/isaac2math/solar_graph_learning}中找到。
英文摘要：
Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using "big data", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \url{https://github.com/isaac2math/solar_graph_learning}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
