Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

@article{Strobl2017ApproximateKC,
  title={Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery},
  author={Eric V. Strobl and Kaidi Zhang and Shyam Visweswaran},
  journal={Journal of Causal Inference},
  year={2017},
  volume={7}
}
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test scales at least quadratically with sample size.We therefore devise two relaxations called the Randomized Conditional Independence Test (RCIT) and the Randomized conditional… CONTINUE READING
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