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.
翻译:这项工作扩展了与随机输入空间的代表理论对因果推断的因果推断。 我们提出了一个基于随机输入空间的因果推断变数的新办法。 我们在观察研究中估算了可能与顺序、反复测量互相依存和时间变化的潜在因果推断人的因果估计效应。 我们的方法扩展了目前假设独立、非时性、潜伏的因果推断人和潜在偏差的估测人的工作。 我们引入了一个简单而优雅的算法,没有模型组件的参数规格。 我们的方法避免了在部署复杂模型(如深神经网络)时花费昂贵和谨慎的参数,以推断现有方法中的因果。 我们展示了我们对于各种基准时间数据集的方法的有效性。