Discovery and Visualization of Nonstationary Causal Models

2015/09/27 by Kun Zhang, Biwei Huang, Zhang, Kun +7
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Biological sciences #FOS: Computer and information sciences #Methodology (stat.ME) #Neurons and Cognition (q-bio.NC) #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.1509.08056

openalex publication_date 2015/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

It is commonplace to encounter nonstationary data, of which the underlying generating process may change over time or across domains. The nonstationarity presents both challenges and opportunities for causal discovery. In this paper we propose a principled framework to handle nonstationarity, and develop some methods to address three important questions. First, we propose an enhanced constraint-based method to detect variables whose local mechanisms are nonstationary and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine some causal directions by taking advantage of information carried by changing distributions. Third, we develop a method for visualizing the nonstationarity of causal modules. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

Citations

• Kernel-based Conditional Independence Test and Application in Causal Discovery

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