causal_falsify.algorithms.hgic module

class causal_falsify.algorithms.hgic.HGIC(cond_indep_test='kcit_rbf', max_tests=-1, min_test_sample_size=25, method_pval_combination='tippett')[source]

Bases: AbstractFalsificationAlgorithm

test(data, covariate_vars, treatment_var, outcome_var, source_var)[source]

Perform falsification test for joint test of unconfoundedness and independence of causal mechanisms.

Parameters:

  • data (pd.DataFrame) – DataFrame containing all required columns.

  • covariate_vars (List[str]) – Covariate column names to condition on.

  • treatment_var (str) – Treatment column name.

  • outcome_var (str) – Outcome column name.

  • source_var (str) – Source/environment indicator column name.

Returns:

p-value of the falsification test; low p-value implies unmeasured confounding may be present.

Return type:

float