Discovery and Visualization of Nonstationary Causal Models
By Kun Zhang, Biwei Huang, Jiji Zhang, Bernhard Schölkopf, Clark Glymour. 2015
15 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bernhard Schölkopf
- Kun Zhang
- Clark Glymour
- Jan Peters
- Biwei Huang
- Jiji Zhang
- Dominik Janzing
- J. Ramsey
In Computational Theory and Mathematics
- Kun Zhang
- Bernhard Schölkopf
- Clark Glymour
- Biwei Huang
- Jiji Zhang
- Ruben Sanchez-Romero
- J. Ramsey
- Negar Kiyavash
In Signal Processing
- Bernhard Schölkopf
- Kun Zhang
- Clark Glymour
- Jiji Zhang
- Biwei Huang
- J. Ramsey
- Ruben Sanchez-Romero
- Dominik Janzing
Citations in the corpus
- Causal Discovery from Heterogeneous/Nonstationary Data (2019, background): “…for causal direction identification. it is known that with functional causal model-based approaches, there are cases where causal directions are not identifiable, e.g., the linear-Gaussian case and the case with a general functional class (Hyv¨arinen and Pajunen, 1999; Zhang et al., 2015b).”
- Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, background): “For a complete proof see [Zhang et al. , 2015].”
- Learning Causal Structures Using Regression Invariance (2017, background): “Recently, the authors of [27] considered the setting in which changes in the mechanism of variables prevents ordinary conditional independence based algorithms from discovering the correct structure.”