Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies-猫眼课题宝

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2022-03

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Shachi Deshpande;Kaiwen Wang;Dhruv Sreenivas;Zheng Li;Volodymyr Kuleshov

从观察数据中估计干预的效果，同时考虑混杂变量是因果推理的一个关键任务。通常情况下，混杂因素是未观察到的，但我们可以访问大量额外的非结构化数据（图像，文本），这些数据包含有关缺失混杂因素的有价值的代理信号。本文认为，利用这种非结构化数据可以大大提高因果效应估计的准确性。具体来说，我们引入了深层多模态结构方程，这是一种因果效应估计的生成模型，其中混杂因素是潜在变量，非结构化数据是代理变量。该模型支持多个多模态代理（图像，文本）以及丢失的数据。我们的经验表明，我们的方法优于现有的方法的基础上的倾向分数和纠正混淆使用非结构化的输入任务，在基因组学和医疗保健。我们的方法可以潜在地支持使用大量的数据，这些数据以前没有用于因果推理

Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing confounders. This paper argues that leveraging this unstructured data can greatly improve the accuracy of causal effect estimation. Specifically, we introduce deep multi-modal structural equations, a generative model for causal effect estimation in which confounders are latent variables and unstructured data are proxy variables. This model supports multiple multi-modal proxies (images, text) as well as missing data. We empirically demonstrate that our approach outperforms existing methods based on propensity scores and corrects for confounding using unstructured inputs on tasks in genomics and healthcare. Our methods can potentially support the use of large amounts of data that were previously not used in causal inference

Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies

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