diff_diff.StackedDiDResults#

class diff_diff.StackedDiDResults[source]#

Bases: object

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

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.

Attributes

`alpha`
`anticipation`
`att`
`clean_control`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`conf_int`
`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)#

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)

Return type:

None

classmethod __new__(*args, **kwargs)#