causaldag.utils.ci_tests.partial_correlation_test.partial_correlation_test

causaldag.utils.ci_tests.partial_correlation_test.partial_correlation_test(suffstat: Dict[KT, VT], i, j, cond_set=None, alpha=None)

Test the null hypothesis that i and j are conditionally independent given cond_set.

Uses Fisher’s z-transform.

Parameters:

• suffstat –

  dictionary containing:

  • n – number of samples
  • C – correlation matrix
  • K (optional) – inverse correlation matrix
  • rho (optional) – partial correlation matrix (K, normalized so diagonals are 1).
• i – position of first variable in correlation matrix.
• j – position of second variable in correlation matrix.
• cond_set – positions of conditioning set in correlation matrix.
• alpha – Significance level.

Returns:

dictionary containing:

• statistic
• p_value
• reject

Return type:

dict