diff_diff.PowerResults

class diff_diff.PowerResults[source]

Bases: object

Results from analytical power analysis.

power

Statistical power (probability of rejecting H0 when effect exists).

Type:: float

mde

Minimum detectable effect size.

Type:: float

required_n

Required total sample size (treated + control).

Type:: int

effect_size

Effect size used in calculation.

Type:: float

alpha

Significance level.

Type:: float

alternative

Alternative hypothesis (‘two-sided’, ‘greater’, ‘less’).

Type:: str

n_treated

Number of treated units.

Type:: int

n_control

Number of control units.

Type:: int

n_pre

Number of pre-treatment periods.

Type:: int

n_post

Number of post-treatment periods.

Type:: int

sigma

Residual standard deviation.

Type:: float

rho

Intra-cluster correlation (for panel data).

Type:: float

design

Study design type (‘basic_did’, ‘panel’, ‘staggered’).

Type:: str

__init__(power, mde, required_n, effect_size, alpha, alternative, n_treated, n_control, n_pre, n_post, sigma, rho=0.0, design='basic_did')

Parameters:

power (float)
mde (float)
required_n (int)
effect_size (float)
alpha (float)
alternative (str)
n_treated (int)
n_control (int)
n_pre (int)
n_post (int)
sigma (float)
rho (float)
design (str)

Return type:

None

Methods

`__init__`(power, mde, required_n, ...[, rho, ...])
`print_summary`()	Print the summary to stdout.
`summary`()	Generate a formatted summary of power analysis results.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`design`
`rho`
`power`
`mde`
`required_n`
`effect_size`
`alpha`
`alternative`
`n_treated`
`n_control`
`n_pre`
`n_post`
`sigma`

power: float

mde: float

required_n: int

effect_size: float

alpha: float

alternative: str

n_treated: int

n_control: int

n_pre: int

n_post: int

sigma: float

rho: float = 0.0

design: str = 'basic_did'

__repr__()[source]

Concise string representation.

Return type:: str

summary()[source]

Generate a formatted summary of power analysis results.

Returns:: Formatted summary table.
Return type:: str

print_summary()[source]

Print the summary to stdout.

Return type:: None

to_dict()[source]

Convert results to a dictionary.

Returns:: Dictionary containing all power analysis results.
Return type:: Dict[str, Any]

to_dataframe()[source]

Convert results to a pandas DataFrame.

Returns:: DataFrame with power analysis results.
Return type:: pd.DataFrame

__init__(power, mde, required_n, effect_size, alpha, alternative, n_treated, n_control, n_pre, n_post, sigma, rho=0.0, design='basic_did')

Parameters:

power (float)
mde (float)
required_n (int)
effect_size (float)
alpha (float)
alternative (str)
n_treated (int)
n_control (int)
n_pre (int)
n_post (int)
sigma (float)
rho (float)
design (str)

Return type:

None