Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets
含潜变量的单一或重叠数据集中的分层背景知识与约束式因果发现
In this paper we consider the use of tiered background knowledge within constraint based causal discovery. Our focus is on settings relaxing causal sufficiency, i.e. allowing for latent variables which may arise because relevant information could not be measured at all, or not jointly, as in the case of multiple overlapping datasets. We first present novel insights into the properties of the 'tiered FCI' (tFCI) algorithm. Building on this, we introduce a new extension of the…