diff_diff.DiDResults#

class diff_diff.DiDResults[source]#

Bases: object

Results from a Difference-in-Differences estimation.

Provides easy access to coefficients, standard errors, confidence intervals, and summary statistics in a Pythonic way.

att#

Average Treatment effect on the Treated (ATT).

Type:: float

se#

Standard error of the ATT estimate.

Type:: float

t_stat#

T-statistic for the ATT estimate.

Type:: float

p_value#

P-value for the null hypothesis that ATT = 0.

Type:: float

conf_int#

Confidence interval for the ATT.

Type:: tuple[float, float]

n_obs#

Number of observations used in estimation.

Type:: int

n_treated#

Number of treated units/observations.

Type:: int

n_control#

Number of control units/observations.

Type:: int

Methods

`__init__`(att, se, t_stat, p_value, conf_int, ...)
`print_summary`([alpha])	Print the summary to stdout.
`summary`([alpha])	Generate a formatted summary of the estimation results.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`alpha`
`bootstrap_distribution`
`cluster_name`
`coef_var`	SE / abs(ATT).
`coefficients`
`conley_lag_cutoff`
`fitted_values`
`inference_method`
`is_significant`	Check if the ATT is statistically significant at the alpha level.
`n_bootstrap`
`n_clusters`
`r_squared`
`residuals`
`significance_stars`	Return significance stars based on p-value.
`survey_metadata`
`vcov`
`vcov_type`
`att`
`se`
`t_stat`
`p_value`
`conf_int`
`n_obs`
`n_treated`
`n_control`

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated, n_control, alpha=0.05, coefficients=None, vcov=None, residuals=None, fitted_values=None, r_squared=None, inference_method='analytical', n_bootstrap=None, n_clusters=None, bootstrap_distribution=None, survey_metadata=None, vcov_type=None, cluster_name=None, conley_lag_cutoff=None)#

Parameters:

att (float)
se (float)
t_stat (float)
p_value (float)
conf_int (Tuple[float, float])
n_obs (int)
n_treated (int)
n_control (int)
alpha (float)
coefficients (Dict[str, float] | None)
vcov (ndarray | None)
residuals (ndarray | None)
fitted_values (ndarray | None)
r_squared (float | None)
inference_method (str)
n_bootstrap (int | None)
n_clusters (int | None)
bootstrap_distribution (ndarray | None)
survey_metadata (Any | None)
vcov_type (str | None)
cluster_name (str | None)
conley_lag_cutoff (int | None)

Return type:

None

classmethod __new__(*args, **kwargs)#