diff_diff.CallawaySantAnnaResults#

class diff_diff.CallawaySantAnnaResults[source]#

Bases: object

Results from Callaway-Sant’Anna (2021) staggered DiD estimation.

This class stores group-time average treatment effects ATT(g,t) and provides methods for aggregation into summary measures.

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_p_value#

P-value for overall ATT.

Type:: float

overall_conf_int#

Confidence interval for overall ATT.

Type:: tuple

groups#

List of treatment 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 ever-treated units.

Type:: int

n_control_units#

Number of never-treated units (excludes not-yet-treated dynamic controls).

Type:: int

event_study_effects#

Effects aggregated by relative time (event study).

Type:: dict, optional

group_effects#

Effects aggregated by treatment cohort.

Type:: dict, optional

pscore_trim#

Propensity score trimming bound used during estimation.

Type:: float

vcov_type#

Variance type used during estimation. CallawaySantAnna is permanently narrow to "hc1" — see REGISTRY.md “IF-based variance estimators vs analytical-sandwich estimators” for why analytical-sandwich families don’t compose with the per-(g,t) doubly-robust / IPW / outcome-regression structure.

Type:: str

cluster_name#

Canonical cluster column. Set to survey_design.psu when an explicit survey PSU was provided (regardless of bare cluster=), otherwise to self.cluster when bare cluster synthesizes or injects a PSU. None when no clustering is active.

Type:: str, optional

n_clusters#

Number of unique clusters (PSUs) used for variance estimation. None when no clustering is active.

Type:: int, optional

df_inference#

Cluster-level degrees of freedom for downstream inference (e.g., HonestDiD t-critical-value selection) on the bare-cluster= synthesize path ONLY (the case where survey_metadata is intentionally None to preserve the survey/non-survey contract for DiagnosticReport / summary()). When the user provides an explicit survey_design= (inject or conflict branches), df_inference stays None and the canonical df carrier is survey_metadata.df_survey — which holds the actual CS-internal df, including any post-resolve tightening (e.g., the overall_effective_df recompute for replicate aggregations). HonestDiD reads survey_metadata.df_survey first and falls back to df_inference only when survey_metadata is absent. Narrow contract prevents HonestDiD from silently overriding a tightened survey df with the original resolved_survey.df_survey.

Type:: int, 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.

Attributes

`alpha`
`anticipation`
`att`
`base_period`
`bootstrap_results`
`cband_crit_value`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`conf_int`
`control_group`
`df_inference`
`epv_diagnostics`
`epv_threshold`
`event_study_effects`
`event_study_vcov`
`event_study_vcov_index`
`group_effects`
`influence_functions`
`is_significant`	Check if overall ATT is significant.
`n_clusters`
`p_value`
`panel`
`pscore_fallback`
`pscore_trim`
`se`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`vcov_type`
`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`

__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, alpha=0.05, control_group='never_treated', base_period='varying', anticipation=0, panel=True, event_study_effects=None, group_effects=None, influence_functions=None, event_study_vcov=None, event_study_vcov_index=None, bootstrap_results=None, cband_crit_value=None, pscore_trim=0.01, survey_metadata=None, epv_diagnostics=None, epv_threshold=10, pscore_fallback='error', vcov_type='hc1', cluster_name=None, n_clusters=None, df_inference=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)
alpha (float)
control_group (str)
base_period (str)
anticipation (int)
panel (bool)
event_study_effects (Dict[int, Dict[str, Any]] | None)
group_effects (Dict[Any, Dict[str, Any]] | None)
influence_functions (np.ndarray | None)
event_study_vcov (np.ndarray | None)
event_study_vcov_index (list | None)
bootstrap_results (CSBootstrapResults | None)
cband_crit_value (float | None)
pscore_trim (float)
survey_metadata (Any | None)
epv_diagnostics (Dict[Tuple[Any, Any], Dict[str, Any]] | None)
epv_threshold (float)
pscore_fallback (str)
vcov_type (str)
cluster_name (str | None)
n_clusters (int | None)
df_inference (int | None)

Return type:

None

classmethod __new__(*args, **kwargs)#