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

__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', bootstrap_results=None, cohort_effects=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)
bootstrap_results (SABootstrapResults | None)
cohort_effects (Dict[Tuple[Any, int], Dict[str, Any]] | None)

Return type:

None

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`
`bootstrap_results`
`cohort_effects`
`control_group`
`is_significant`	Check if overall ATT is significant.
`significance_stars`	Significance stars for overall ATT.
`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`

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 = 0.05

control_group: str = 'never_treated'

bootstrap_results: SABootstrapResults | None = None

cohort_effects: Dict[Tuple[Any, int], Dict[str, Any]] | None = None

__repr__()[source]

Concise string representation.

Return type:: str

summary(alpha=None)[source]

Generate formatted summary of estimation results.

Parameters:: alpha (float, optional) – Significance level. Defaults to alpha used in estimation.
Returns:: Formatted summary.
Return type:: str

print_summary(alpha=None)[source]

Print summary to stdout.

Parameters:: alpha (float | None)
Return type:: None

to_dataframe(level='event_study')[source]

Convert results to DataFrame.

Parameters:: level (str, default="event_study") – Level of aggregation: “event_study” or “cohort”.
Returns:: Results as DataFrame.
Return type:: pd.DataFrame

property is_significant: bool: Check if overall ATT is significant.

property significance_stars: str: Significance stars for overall ATT.

__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', bootstrap_results=None, cohort_effects=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)
bootstrap_results (SABootstrapResults | None)
cohort_effects (Dict[Tuple[Any, int], Dict[str, Any]] | None)

Return type:

None