OutcomeFalsification#

class causalpy.checks.outcome_falsification.OutcomeFalsification[source]#

Outcome falsification sensitivity check.

Re-fits the experiment with alternative formulas and reports the estimated effect size for each. This is an informational check – it does not make pass/fail judgments. The researcher compares effect sizes across outcomes to assess whether the pattern is consistent with their causal story.

Parameters:

  • formulas (list[str]) – Falsification formulas. Each must be a complete patsy formula (e.g., "beer ~ 1 + year"). The outcome variable must exist in the data.

  • alpha (float) – Significance level. The HDI probability is 1 - alpha.

Examples

>>> import causalpy as cp
>>> check = cp.checks.OutcomeFalsification(
...     formulas=["beer ~ 1 + year", "lnincome ~ 1 + year"],
... )

Methods

OutcomeFalsification.__init__(formulas[, alpha])

OutcomeFalsification.run(experiment, context)

Run outcome falsification analysis.

OutcomeFalsification.validate(experiment)

Verify the experiment is compatible with OutcomeFalsification.

Attributes

applicable_methods

__init__(formulas, alpha=0.05)[source]#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#