DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
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发表时间：
2018-02
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通讯作者：
Nathan Kallus
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作者：
Nathan Kallus
我们研究了最佳协变量平衡的因果关系从观测数据时，丰富的协变量和复杂的关系，需要灵活的建模与神经网络。标准的方法，如倾向加权和匹配/平衡失败，在这样的设置，由于错误校准的倾向网和不适当的协变量表示，分别。我们提出了一种新的方法，基于对抗性训练的加权和一个神经网络，有效地解决了这一方法上的差距。通过该方法的新理论表征以及使用全连接架构学习复杂关系和卷积架构处理图像混淆的经验结果来证明这一点，展示了这种新方法如何在这些具有挑战性的环境中实现强大的因果分析。
We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inappropriate covariate representations, respectively. We propose a new method based on adversarial training of a weighting and a discriminator network that effectively addresses this methodological gap. This is demonstrated through new theoretical characterizations of the method as well as empirical results using both fully connected architectures to learn complex relationships and convolutional architectures to handle image confounders, showing how this new method can enable strong causal analyses in these challenging settings.