diff_diff.StaggeredTripleDiffResults#

class diff_diff.StaggeredTripleDiffResults[source]#

Bases: object

Results from Staggered Triple Difference (DDD) estimation.

Implements the Ortiz-Villavicencio & Sant’Anna (2025) estimator for staggered adoption settings with an eligibility dimension.

group_time_effects#

Dictionary mapping (group, time) tuples to effect dictionaries.

Type:: dict

overall_att#

Overall average treatment effect (weighted average of ATT(g,t)).

Type:: float

overall_se#

Standard error of overall ATT.

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

groups#

List of enabling cohorts (first treatment periods).

Type:: list

time_periods#

List of all time periods.

Type:: list

n_obs#

Total number of observations.

Type:: int

n_treated_units#

Number of treated units (S < inf AND Q = 1).

Type:: int

n_control_units#

Number of units not in treated group.

Type:: int

n_never_enabled#

Number of never-enabled units (S = inf or 0).

Type:: int

n_eligible#

Number of eligible units (Q = 1).

Type:: int

n_ineligible#

Number of ineligible units (Q = 0).

Type:: int

overall_att_es#

Paper Eq. (4.14) “overall” ATT: the unweighted mean of the post-treatment event-study effects ES(e). Populated only when aggregate is "event_study" or "all"; None otherwise. Distinct from overall_att (the Callaway-Sant’Anna simple post-treatment (g,t) average, which is the default headline ATT).

Type:: float, optional

overall_se_es, overall_t_stat_es, overall_p_value_es, overall_conf_int_es

Standard error, t-statistic, p-value, and confidence interval for overall_att_es; None when overall_att_es is None.

Type:: optional

Methods

`__init__`(group_time_effects, overall_att, ...)
`epv_summary`([show_all])	Return per-cohort EPV diagnostics as a DataFrame.
`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 results to dictionary.

Attributes

`alpha`
`anticipation`
`att`
`base_period`
`bootstrap_results`
`cband_crit_value`
`coef_var`	SE / abs(overall ATT).
`comparison_group_counts`
`conf_int`
`control_group`
`epv_diagnostics`
`epv_threshold`
`estimation_method`
`event_study_effects`
`gmm_weights`
`group_effects`
`influence_functions`
`is_significant`	Check if overall ATT is significant.
`overall_att_es`
`overall_conf_int_es`
`overall_p_value_es`
`overall_se_es`
`overall_t_stat_es`
`p_value`
`pscore_fallback`
`pscore_trim`
`se`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`group_time_effects`
`overall_att`
`overall_se`
`overall_t_stat`
`overall_p_value`
`overall_conf_int`
`groups`
`time_periods`
`n_obs`
`n_treated_units`
`n_control_units`
`n_never_enabled`
`n_eligible`
`n_ineligible`

__init__(group_time_effects, overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, groups, time_periods, n_obs, n_treated_units, n_control_units, n_never_enabled, n_eligible, n_ineligible, alpha=0.05, control_group='notyettreated', base_period='varying', anticipation=0, estimation_method='dr', event_study_effects=None, group_effects=None, influence_functions=None, bootstrap_results=None, cband_crit_value=None, pscore_trim=0.01, survey_metadata=None, comparison_group_counts=None, gmm_weights=None, epv_diagnostics=None, epv_threshold=10, pscore_fallback='error', overall_att_es=None, overall_se_es=None, overall_t_stat_es=None, overall_p_value_es=None, overall_conf_int_es=None)#

Parameters:

group_time_effects (Dict[Tuple[Any, Any], Dict[str, Any]])
overall_att (float)
overall_se (float)
overall_t_stat (float)
overall_p_value (float)
overall_conf_int (Tuple[float, float])
groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_treated_units (int)
n_control_units (int)
n_never_enabled (int)
n_eligible (int)
n_ineligible (int)
alpha (float)
control_group (str)
base_period (str)
anticipation (int)
estimation_method (str)
event_study_effects (Dict[int, Dict[str, Any]] | None)
group_effects (Dict[Any, Dict[str, Any]] | None)
influence_functions (np.ndarray | None)
bootstrap_results (CSBootstrapResults | None)
cband_crit_value (float | None)
pscore_trim (float)
survey_metadata (Any | None)
comparison_group_counts (Dict[Tuple, int] | None)
gmm_weights (Dict[Tuple, Dict] | None)
epv_diagnostics (Dict[Tuple[Any, Any], Dict[str, Any]] | None)
epv_threshold (float)
pscore_fallback (str)
overall_att_es (float | None)
overall_se_es (float | None)
overall_t_stat_es (float | None)
overall_p_value_es (float | None)
overall_conf_int_es (Tuple[float, float] | None)

Return type:

None

classmethod __new__(*args, **kwargs)#