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Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse.

Camil Băncioiu1, Remus Brad1

  • 1Department of Computer Science and Electrical Engineering, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

A new method efficiently computes the statistical G-test by linking it to information theory. This approach significantly speeds up applications like feature selection and causal inference by reusing calculations.

Keywords:
G-testMarkov blanketcausal inferencecomputation reusefeature selectioninformation theory

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Area of Science:

  • Computational statistics
  • Information theory
  • Data science

Background:

  • The G-test is a statistical test used in various data science applications.
  • Existing methods for computing the G-test can be computationally intensive, especially in iterative processes.
  • Applications like feature selection and causal inference often require numerous G-tests.

Purpose of the Study:

  • To introduce a novel and highly efficient method for computing the statistical G-test.
  • To demonstrate the computational advantages of this new method in data science tasks.

Main Methods:

  • The G-test statistic is decomposed into a sum of joint entropy terms, leveraging information theory principles.
  • This decomposition allows for the reuse of partial results, optimizing computation.
  • The method was implemented within the IPC-MB algorithm for Markov blanket discovery.

Main Results:

  • The proposed G-test computation method demonstrated significant efficiency gains compared to unoptimized methods.
  • IPC-MB using the enhanced G-test showed superior performance over its standard version and IPC-MB++.
  • The method offers substantial speedups for iterative G-test computations.

Conclusions:

  • The novel G-test computation method offers substantial efficiency improvements.
  • This approach is broadly applicable to any data science task involving G-tests, particularly feature selection and causal inference.
  • The method's foundation in information theory ensures wide applicability across data science domains.