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#

Within-unit (serial) equicorrelation (Burlig 2020 Eq. 2 equicorrelated case).

Type:: float

deff#

Survey design effect (variance inflation factor).

Type:: float

design#

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

Type:: str

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

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

__init__(power, mde, required_n, effect_size, alpha, alternative, n_treated, n_control, n_pre, n_post, sigma, rho=0.0, deff=1.0, 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)
deff (float)
design (str)

Return type:

None

classmethod __new__(*args, **kwargs)#