Exact model-free function inference using uniform marginal counts for null population.

ABSTRACT:

Motivation

Recognizing cause-effect relationships is a fundamental inquiry in science. However, current causal inference methods often focus on directionality but not statistical significance. A ramification is chance patterns of uneven marginal distributions achieving a perfect directionality score.

Results

To overcome such issues, we design the uniform exact function test with continuity correction (UEFTC) to detect functional dependency between two discrete random variables. The null hypothesis is two variables being statistically independent. Unique from related tests whose null populations use observed marginals, we define the null population by an embedded uniform square. We also present a fast algorithm to accomplish the test. On datasets with ground truth, the UEFTC

SUBMITTER: Li Y 

PROVIDER: S-EPMC11972114 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature