Discovery and Visualization of Nonstationary Causal Models

@inproceedings{Zhang2015DiscoveryAV,
  title={Discovery and Visualization of Nonstationary Causal Models},
  author={Kun Zhang and Biwei Huang and Jiji Zhang and Bernhard Scholkopf and Clark Glymour},
  year={2015}
}
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… CONTINUE READING
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Key Quantitative Results

  • We found that our method effectively reduces the FP rate, from 62.86% to 17.14%, compared to the original constraint-based method with SGS search and KCI-test.

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