Power analysis for causal discovery

Erich Kummerfeld, Leland Williams, Sisi Ma

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms. The power sample characteristics of causal discovery algorithms typically cannot be described by a closed formula, but we resolve this problem by developing a new power sample analysis method based on standardized in silico simulation experiments. Our procedure generates data with carefully controlled statistical effect sizes in order to enable an accurate numerical power sample analysis. We present that method, apply it to generate an initial power analysis table, provide a web interface for searching this table, and show how the table or web interface can be used to solve several types of real-world power analysis problems, such as sample size planning, interpretation of results, and sensitivity analysis.

Original languageEnglish (US)
Pages (from-to)289-304
Number of pages16
JournalInternational Journal of Data Science and Analytics
Volume17
Issue number3
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

Keywords

  • Causal discovery
  • Graphical models
  • Power analysis
  • Simulation

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