diff_diff.StackedDiDResults#

class diff_diff.StackedDiDResults[source]#

Bases: BaseResults

Results from Stacked DiD estimation (Wing, Freedman & Hollingsworth 2024).

overall_att#

Overall average treatment effect on the treated (average of post-treatment event-study coefficients).

Type:: float

overall_se#

Standard error of overall ATT (delta method on VCV).

Type:: float

overall_t_stat#

T-statistic for overall ATT.

Type:: float

overall_p_value#

P-value for overall ATT.

Type:: float

overall_conf_int#

Confidence interval for overall ATT.

Type:: tuple

event_study_effects#

Dictionary mapping event time h to effect dict with keys: ‘effect’, ‘se’, ‘t_stat’, ‘p_value’, ‘conf_int’, ‘n_obs’.

Type:: dict, optional

group_effects#

Dictionary mapping cohort g to effect dict.

Type:: dict, optional

stacked_data#

Full stacked dataset with _sub_exp, _event_time, _D_sa, _Q_weight columns. Accessible for custom analysis.

Type:: pd.DataFrame

groups#

Adoption events in the trimmed set (Omega_kappa).

Type:: list

trimmed_groups#

Adoption events excluded by IC1/IC2.

Type:: list

time_periods#

All time periods in the original data.

Type:: list

n_obs#

Number of observations in the original data.

Type:: int

n_stacked_obs#

Number of observations in the stacked dataset.

Type:: int

n_sub_experiments#

Number of sub-experiments in the stack.

Type:: int

n_treated_units#

Distinct treated units across trimmed set.

Type:: int

n_control_units#

Distinct control units across trimmed set.

Type:: int

kappa_pre#

Pre-treatment event-time window size.

Type:: int

kappa_post#

Post-treatment event-time window size.

Type:: int

weighting#

Weighting scheme used.

Type:: str

clean_control#

Clean control definition used.

Type:: str

alpha#

Significance level used.

Type:: float

event_study_vcov#

Full event-study variance-covariance matrix: the sub-block of the pooled stacked-regression coefficient covariance over the estimated D_sa x event-time interaction columns, ordered by event_study_vcov_index. The reported per-event-time SEs are exactly sqrt(diag()) of this matrix in every inference mode (analytical hc1/hc2_bm sandwich, survey replicate refit, and survey TSL all produce the coefficient covariance the SEs are read from). The reference period is synthesized, never a regression column, so it is absent from the index. None when no event study was requested.

Type:: np.ndarray, optional

event_study_vcov_index#

Event-time labels ordering event_study_vcov’s rows/columns (the estimated event times, reference excluded).

Type:: list of int, optional

event_study_df#

Per-event-time inference degrees of freedom PROVENANCE: maps each estimated event time to the df actually passed to safe_inference for its stored p-value/CI (per-event Bell-McCaffrey Satterthwaite df under hc2_bm; the scalar survey df under survey designs), or NaN when the row used normal theory, the df was undefined, or hc2_bm failed closed. None when no event study was requested.

Type:: dict, optional

Methods

`__init__`(overall_att, overall_se, ...[, ...])
`print_summary`([alpha])	Print summary to stdout.
`summary`([alpha])	Generate formatted summary of estimation results.
`to_dataframe`([level])	Convert results to DataFrame.
`to_dict`()	Convert headline results to a dictionary.

Attributes

`alpha`
`anticipation`
`att`
`balance`
`balance_diagnostics`
`clean_control`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`conf_int`
`covariates`
`event_study_df`
`event_study_vcov`
`event_study_vcov_index`
`is_significant`	Check if overall ATT is significant.
`kappa_post`
`kappa_pre`
`n_clusters`
`n_control_units`
`n_obs`
`n_stacked_obs`
`n_sub_experiments`
`n_treated_units`
`p_value`
`se`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`vcov_type`
`weighting`
`overall_att`
`overall_se`
`overall_t_stat`
`overall_p_value`
`overall_conf_int`
`event_study_effects`
`group_effects`
`stacked_data`
`groups`
`trimmed_groups`
`time_periods`

__init__(overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, event_study_effects, group_effects, stacked_data, groups=<factory>, trimmed_groups=<factory>, time_periods=<factory>, n_obs=0, n_stacked_obs=0, n_sub_experiments=0, n_treated_units=0, n_control_units=0, kappa_pre=1, kappa_post=1, weighting='aggregate', clean_control='not_yet_treated', alpha=0.05, anticipation=0, vcov_type='hc1', cluster_name=None, n_clusters=None, survey_metadata=None, balance='none', covariates=None, balance_diagnostics=None, event_study_vcov=None, event_study_vcov_index=None, event_study_df=None)#

Parameters:

overall_att (float)
overall_se (float)
overall_t_stat (float)
overall_p_value (float)
overall_conf_int (Tuple[float, float])
event_study_effects (Dict[int, Dict[str, Any]] | None)
group_effects (Dict[Any, Dict[str, Any]] | None)
stacked_data (DataFrame)
groups (List[Any])
trimmed_groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_stacked_obs (int)
n_sub_experiments (int)
n_treated_units (int)
n_control_units (int)
kappa_pre (int)
kappa_post (int)
weighting (str)
clean_control (str)
alpha (float)
anticipation (int)
vcov_type (str)
cluster_name (str | None)
n_clusters (int | None)
survey_metadata (Any | None)
balance (str)
covariates (List[str] | None)
balance_diagnostics (Dict[Any, Dict[str, Any]] | None)
event_study_vcov (ndarray | None)
event_study_vcov_index (List[int] | None)
event_study_df (Dict[int, float] | None)

Return type:

None

classmethod __new__(*args, **kwargs)#