diff_diff.compute_mdv#

diff_diff.compute_mdv(results, alpha=0.05, target_power=0.8, violation_type='linear', pre_periods=None, violation_weights=None, pretest_form='nis')[source]

Compute minimum detectable violation.

Parameters:

  • results (results object) – Event study results.

  • alpha (float, default=0.05) – Significance level.

  • target_power (float, default=0.80) – Target power for MDV calculation.

  • violation_type (str, default='linear') – Type of violation pattern: linear / constant / last_period / custom. For custom, also pass violation_weights.

  • pre_periods (list of int, optional) – Explicit list of pre-treatment periods. If None, attempts to infer from results. Use when you’ve estimated all periods as post_periods.

  • violation_weights (np.ndarray, optional) – Custom violation pattern weights. Required when violation_type='custom'; ignored for other violation types.

  • pretest_form ({'nis', 'wald'}, default='nis') – Pretest acceptance-region form. See compute_pretrends_power and PreTrendsPower for the NIS-vs-Wald discussion.

Returns:

Minimum detectable violation.

Return type:

float