diff_diff.TripleDifferenceResults#

class diff_diff.TripleDifferenceResults[source]#

Bases: object

Results from Triple Difference (DDD) estimation.

Provides access to the estimated average treatment effect on the treated (ATT), standard errors, confidence intervals, and diagnostic information.

att#

Average Treatment effect on the Treated (ATT). This is the effect on units in the treated group (G=1) and eligible partition (P=1) after treatment (T=1).

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#

Total number of observations used in estimation.

Type:: int

n_treated_eligible#

Number of observations in treated group and eligible partition.

Type:: int

n_treated_ineligible#

Number of observations in treated group and ineligible partition.

Type:: int

n_control_eligible#

Number of observations in control group and eligible partition.

Type:: int

n_control_ineligible#

Number of observations in control group and ineligible partition.

Type:: int

estimation_method#

Estimation method used: “dr” (doubly robust), “reg” (regression adjustment), or “ipw” (inverse probability weighting).

Type:: str

alpha#

Significance level used for confidence intervals.

Type:: float

Methods

`__init__`(att, se, t_stat, p_value, conf_int, ...)
`epv_summary`([show_all])	Return per-subgroup EPV diagnostics as a DataFrame.
`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`
`cluster_name`
`covariate_balance`
`epv_diagnostics`
`epv_threshold`
`group_means`
`inference_method`
`is_significant`	Check if the ATT is statistically significant at the alpha level.
`n_bootstrap`
`n_clusters`
`pscore_fallback`
`pscore_stats`
`r_squared`
`significance_stars`	Return significance stars based on p-value.
`survey_metadata`
`vcov_type`
`att`
`se`
`t_stat`
`p_value`
`conf_int`
`n_obs`
`n_treated_eligible`
`n_treated_ineligible`
`n_control_eligible`
`n_control_ineligible`
`estimation_method`

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated_eligible, n_treated_ineligible, n_control_eligible, n_control_ineligible, estimation_method, alpha=0.05, group_means=None, pscore_stats=None, r_squared=None, covariate_balance=None, inference_method='analytical', vcov_type='hc1', cluster_name=None, n_bootstrap=None, n_clusters=None, survey_metadata=None, epv_diagnostics=None, epv_threshold=10, pscore_fallback='error')#

Parameters:

att (float)
se (float)
t_stat (float)
p_value (float)
conf_int (Tuple[float, float])
n_obs (int)
n_treated_eligible (int)
n_treated_ineligible (int)
n_control_eligible (int)
n_control_ineligible (int)
estimation_method (str)
alpha (float)
group_means (Dict[str, float] | None)
pscore_stats (Dict[str, float] | None)
r_squared (float | None)
covariate_balance (DataFrame | None)
inference_method (str)
vcov_type (str)
cluster_name (str | None)
n_bootstrap (int | None)
n_clusters (int | None)
survey_metadata (Any | None)
epv_diagnostics (Dict[int, Dict[str, Any]] | None)
epv_threshold (float)
pscore_fallback (str)

Return type:

None

classmethod __new__(*args, **kwargs)#