Adversarial Generalized Method of Moments
Adversarial Generalized Method of Moments
复制标题
对抗性广义矩量法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Vasilis Syrgkanis
中科院分区：
文献类型：
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作者：
Greg Lewis;Vasilis Syrgkanis
我们提供了一种学习通过条件矩限制描述的模型的深层神经网络表示的方法。条件矩限制被广泛使用，因为它们是社会科学家用来描述他们为进行因果推理而做出的假设的语言。我们将估计下属模型的问题描述为建模者和对手之间的零和博弈，并应用对抗性训练。我们的方法在本质上类似于生成性对手网络(GAN)，尽管在这里，建模者正在学习满足矩条件连续体的函数的表示，而对手正在识别违反矩。我们概述了在实践中构建有效对手的方法，包括以k-均值聚类为中心的核和随机森林。我们检验了我们的方法在非参数工具变量回归设置中的实际性能。
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of estimating the underling model as a zero-sum game between a modeler and an adversary and apply adversarial training. Our approach is similar in nature to Generative Adversarial Networks (GAN), though here the modeler is learning a representation of a function that satisfies a continuum of moment conditions and the adversary is identifying violating moments. We outline ways of constructing effective adversaries in practice, including kernels centered by k-means clustering, and random forests. We examine the practical performance of our approach in the setting of non-parametric instrumental variable regression.