Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation
and Orientation Determination
Abstract
It is commonplace to encounter nonstationary or
heterogeneous data, of which the underlying generating process changes over time or across data sets
(the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and
opportunities for causal discovery. In this paper we
develop a principled framework for causal discovery from such data, called Constraint-based causal
Discovery from Nonstationary/heterogeneous Data
(CD-NOD), which addresses two important questions. First, we propose an enhanced constraintbased procedure to detect variables whose local
mechanisms change and recover the skeleton of
the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes
in the data distribution implied by the underlying
causal model, benefiting from information carried
by changing distributions. Experimental results on
various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods