diff_diff.SunAbrahamResults#

class diff_diff.SunAbrahamResults[source]#

Bases: object

Results from Sun-Abraham (2021) interaction-weighted estimation.

event_study_effects#

Dictionary mapping relative time to effect dictionaries with keys: ‘effect’, ‘se’, ‘t_stat’, ‘p_value’, ‘conf_int’, ‘n_groups’.

Type:: dict

overall_att#

Overall average treatment effect (weighted average of post-treatment effects).

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

cohort_weights#

Dictionary mapping relative time to cohort weight dictionaries.

Type:: dict

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.

Type:: int

alpha#

Significance level used for confidence intervals.

Type:: float

control_group#

Type of control group used.

Type:: str

vcov_type#

Variance-covariance family from the fit-time configuration (classical, hc1, hc2, hc2_bm, or conley). On the "conley" (spatial-HAC) path, conley_lag_cutoff and cluster_name are populated. Note: when a survey_design= is supplied, the survey-design Taylor Series Linearization (or replicate-weight refit) variance overrides this analytical family — the field still records the configured value but survey_metadata indicates the survey path was active. Likewise, on bootstrap fits (n_bootstrap > 0) the SE comes from the pairs bootstrap (or Rao-Wu rescaled bootstrap under stratified / PSU survey designs), not the analytical family.

Type:: str

Methods

`__init__`(event_study_effects, overall_att, ...)
`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`
`bootstrap_results`
`cluster_name`
`coef_var`	SE / abs(overall ATT).
`cohort_effects`
`conf_int`
`conley_lag_cutoff`
`control_group`
`event_study_vcov`
`event_study_vcov_index`
`is_significant`	Check if overall ATT is significant.
`p_value`
`se`
`significance_stars`	Significance stars for overall ATT.
`survey_metadata`
`t_stat`
`vcov_type`
`event_study_effects`
`overall_att`
`overall_se`
`overall_t_stat`
`overall_p_value`
`overall_conf_int`
`cohort_weights`
`groups`
`time_periods`
`n_obs`
`n_treated_units`
`n_control_units`

__init__(event_study_effects, overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, cohort_weights, groups, time_periods, n_obs, n_treated_units, n_control_units, alpha=0.05, control_group='never_treated', vcov_type='hc1', anticipation=0, bootstrap_results=None, cohort_effects=None, survey_metadata=None, event_study_vcov=None, event_study_vcov_index=None, conley_lag_cutoff=None, cluster_name=None)#

Parameters:

event_study_effects (Dict[int, Dict[str, Any]])
overall_att (float)
overall_se (float)
overall_t_stat (float)
overall_p_value (float)
overall_conf_int (Tuple[float, float])
cohort_weights (Dict[int, Dict[Any, float]])
groups (List[Any])
time_periods (List[Any])
n_obs (int)
n_treated_units (int)
n_control_units (int)
alpha (float)
control_group (str)
vcov_type (str)
anticipation (int)
bootstrap_results (SABootstrapResults | None)
cohort_effects (Dict[Tuple[Any, int], Dict[str, Any]] | None)
survey_metadata (Any | None)
event_study_vcov (ndarray | None)
event_study_vcov_index (list | None)
conley_lag_cutoff (int | None)
cluster_name (str | None)

Return type:

None

classmethod __new__(*args, **kwargs)#