diff_diff.SimulationPowerResults#

class diff_diff.SimulationPowerResults[source]#

Bases: object

Results from simulation-based power analysis.

power#

Estimated power (proportion of simulations rejecting H0).

Type:: float

power_se#

Standard error of power estimate.

Type:: float

power_ci#

Confidence interval for power estimate.

Type:: Tuple[float, float]

rejection_rate#

Proportion of simulations with p-value < alpha.

Type:: float

mean_estimate#

Mean treatment effect estimate across simulations.

Type:: float

std_estimate#

Standard deviation of estimates across simulations.

Type:: float

mean_se#

Mean standard error across simulations.

Type:: float

coverage#

Proportion of CIs containing true effect.

Type:: float

n_simulations#

Number of simulations performed (successful count; see n_simulation_failures for failed-replicate count).

Type:: int

n_simulation_failures#

Number of simulations at the primary effect size whose estimator.fit (or result extraction) raised an exception and was skipped. Lets callers programmatically detect fragile DGP/estimator pairings; a proportional warning is also emitted above a 10% failure rate.

Type:: int

effect_sizes#

Effect sizes tested (if multiple).

Type:: List[float]

powers#

Power at each effect size (if multiple).

Type:: List[float]

true_effect#

True treatment effect used in simulation.

Type:: float

alpha#

Significance level.

Type:: float

estimator_name#

Name of the estimator used.

Type:: str

effective_n_units#

Effective sample size when it differs from the requested n_units (e.g., due to DDD grid rounding). None when no rounding occurred.

Type:: int or None

Methods

`__init__`(power, power_se, power_ci, ...[, ...])
`power_curve_df`()	Get power curve data as a DataFrame.
`print_summary`()	Print the summary to stdout.
`summary`()	Generate a formatted summary of simulation power results.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`effective_n_units`
`mean_deff`
`mean_icc_realized`
`n_simulation_failures`
`simulation_results`
`survey_config`
`power`
`power_se`
`power_ci`
`rejection_rate`
`mean_estimate`
`std_estimate`
`mean_se`
`coverage`
`n_simulations`
`effect_sizes`
`powers`
`true_effect`
`alpha`
`estimator_name`
`bias`
`rmse`

__init__(power, power_se, power_ci, rejection_rate, mean_estimate, std_estimate, mean_se, coverage, n_simulations, effect_sizes, powers, true_effect, alpha, estimator_name, simulation_results=None, effective_n_units=None, survey_config=None, mean_deff=None, mean_icc_realized=None, n_simulation_failures=0)#

Parameters:

power (float)
power_se (float)
power_ci (Tuple[float, float])
rejection_rate (float)
mean_estimate (float)
std_estimate (float)
mean_se (float)
coverage (float)
n_simulations (int)
effect_sizes (List[float])
powers (List[float])
true_effect (float)
alpha (float)
estimator_name (str)
simulation_results (List[Dict[str, Any]] | None)
effective_n_units (int | None)
survey_config (Any | None)
mean_deff (float | None)
mean_icc_realized (float | None)
n_simulation_failures (int)

Return type:

None

classmethod __new__(*args, **kwargs)#