论文标题
与随机混杂因子的因果建模
Causal Modeling with Stochastic Confounders
论文作者
论文摘要
这项工作扩展了与随机混杂因素的因果推论。我们提出了一种基于具有随机输入空间的代表定理的因果推理的新方法来进行因果推理。我们估计涉及潜在混杂因素的因果效应，这些效应可能是相互依存的，并且与观察性研究中的连续重复测量相互变化。我们的方法扩展了当前的工作，该工作假设具有潜在偏见的估计量的独立，非时空的潜在混杂因素。我们引入了一种简单而优雅的算法，而没有模型组件上的参数规范。我们的方法避免了在部署复杂模型（例如深神经网络）中需要昂贵和仔细的参数化来进行现有方法的因果推断。我们证明了我们的方法对各种基准时间数据集的有效性。
This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space. We estimate causal effects involving latent confounders that may be interdependent and time-varying from sequential, repeated measurements in an observational study. Our approach extends current work that assumes independent, non-temporal latent confounders, with potentially biased estimators. We introduce a simple yet elegant algorithm without parametric specification on model components. Our method avoids the need for expensive and careful parameterization in deploying complex models, such as deep neural networks, for causal inference in existing approaches. We demonstrate the effectiveness of our approach on various benchmark temporal datasets.
