Deep Generalized Method of Moments for Instrumental Variable Analysis
Deep Generalized Method of Moments for Instrumental Variable Analysis
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发表时间：
2019-05
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通讯作者：
Andrew Bennett;Nathan Kallus;Tobias Schnabel
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作者：
Andrew Bennett;Nathan Kallus;Tobias Schnabel
当随机化或完全控制混杂因素不可能时，工具变量分析是估计因果效应的有力工具。当因果效应复杂、工具高维和/或治疗高维时，标准方法（如2SLS、GMM和最近的变体）的应用会受到显著阻碍。在本文中，我们提出了DeepGMM算法来克服这一点。我们的算法是基于一个新的变分GMM与最佳逆协方差加权，使我们能够有效地控制很多时刻的条件。我们进一步开发实用的优化和模型选择技术，使其在实践中特别成功。我们的算法在计算上也是易于处理的，可以处理大规模的数据集。数值结果表明，我们的算法匹配的性能最好的调整方法在标准设置，并继续工作在高维设置，即使最近的方法打破。
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded when the causal effects are complex, the instruments are high-dimensional, and/or the treatment is high-dimensional. In this paper, we propose the DeepGMM algorithm to overcome this. Our algorithm is based on a new variational reformulation of GMM with optimal inverse-covariance weighting that allows us to efficiently control very many moment conditions. We further develop practical techniques for optimization and model selection that make it particularly successful in practice. Our algorithm is also computationally tractable and can handle large-scale datasets. Numerical results show our algorithm matches the performance of the best tuned methods in standard settings and continues to work in high-dimensional settings where even recent methods break.