Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
利用先验知识发现含未观测变量的因果加性模型及其时间序列应用
This paper proposes two methods for causal additive models with unobserved variables (CAM-UV). CAM-UV assumes that the causal functions take the form of generalized additive models and that latent confounders are present. First, we propose a method that leverages prior knowledge for efficient causal discovery. Then, we propose an extension of this method for inferring causality in time series data. The original CAM-UV algorithm differs from other existing causal function…