diff_diff.SimulationMDEResults#

class diff_diff.SimulationMDEResults[source]#

Bases: object

Results from simulation-based minimum detectable effect search.

mde#

Minimum detectable effect (smallest effect achieving target power).

Type:: float

power_at_mde#

Power achieved at the MDE.

Type:: float

target_power#

Target power used in the search.

Type:: float

alpha#

Significance level.

Type:: float

n_units#

Sample size used.

Type:: int

n_simulations_per_step#

Number of simulations per bisection step.

Type:: int

n_steps#

Number of bisection steps performed.

Type:: int

search_path#

Diagnostic trace of {effect_size, power} at each step.

Type:: list of dict

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__`(mde, power_at_mde, target_power, ...)
`summary`()	Generate a formatted summary.
`to_dataframe`()	Convert results to a single-row DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`effective_n_units`
`survey_config`
`mde`
`power_at_mde`
`target_power`
`alpha`
`n_units`
`n_simulations_per_step`
`n_steps`
`search_path`
`estimator_name`

__init__(mde, power_at_mde, target_power, alpha, n_units, n_simulations_per_step, n_steps, search_path, estimator_name, effective_n_units=None, survey_config=None)#

Parameters:

mde (float)
power_at_mde (float)
target_power (float)
alpha (float)
n_units (int)
n_simulations_per_step (int)
n_steps (int)
search_path (List[Dict[str, float]])
estimator_name (str)
effective_n_units (int | None)
survey_config (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#