diff_diff.HonestDiDResults

class diff_diff.HonestDiDResults[source]

Bases: object

Results from Honest DiD sensitivity analysis.

Contains bounds on the treatment effect under the specified restrictions on violations of parallel trends.

lb

Lower bound of identified set.

Type:: float

ub

Upper bound of identified set.

Type:: float

ci_lb

Lower bound of robust confidence interval.

Type:: float

ci_ub

Upper bound of robust confidence interval.

Type:: float

M

The restriction parameter value used.

Type:: float

method

The type of restriction (“smoothness”, “relative_magnitude”, or “combined”).

Type:: str

original_estimate

The original point estimate (under parallel trends).

Type:: float

original_se

The original standard error.

Type:: float

alpha

Significance level for confidence interval.

Type:: float

ci_method

Method used for CI construction (“FLCI” or “C-LF”).

Type:: str

original_results

The original estimation results object.

Type:: Any

__init__(lb, ub, ci_lb, ci_ub, M, method, original_estimate, original_se, alpha=0.05, ci_method='FLCI', original_results=None, event_study_bounds=None)

Parameters:

lb (float)
ub (float)
ci_lb (float)
ci_ub (float)
M (float)
method (str)
original_estimate (float)
original_se (float)
alpha (float)
ci_method (str)
original_results (Any | None)
event_study_bounds (Dict[Any, Dict[str, float]] | None)

Return type:

None

Methods

`__init__`(lb, ub, ci_lb, ci_ub, M, method, ...)
`print_summary`()	Print summary to stdout.
`summary`()	Generate formatted summary of sensitivity analysis results.
`to_dataframe`()	Convert results to DataFrame.
`to_dict`()	Convert results to dictionary.

Attributes

`alpha`
`ci_method`
`ci_width`	Width of the confidence interval.
`event_study_bounds`
`identified_set_width`	Width of the identified set.
`is_significant`	Check if CI excludes zero (effect is robust to violations).
`original_results`
`significance_stars`	Return significance indicator if robust CI excludes zero.
`lb`
`ub`
`ci_lb`
`ci_ub`
`M`
`method`
`original_estimate`
`original_se`

lb: float

ub: float

ci_lb: float

ci_ub: float

M: float

method: str

original_estimate: float

original_se: float

alpha: float = 0.05

ci_method: str = 'FLCI'

original_results: Any | None = None

event_study_bounds: Dict[Any, Dict[str, float]] | None = None

property is_significant: bool: Check if CI excludes zero (effect is robust to violations).

property significance_stars: str

Return significance indicator if robust CI excludes zero.

Note: Unlike point estimation, partial identification does not yield a single p-value. This returns “*” if the robust CI excludes zero at the specified alpha level, indicating the effect is robust to the assumed violations of parallel trends.

property identified_set_width: float: Width of the identified set.

property ci_width: float: Width of the confidence interval.

summary()[source]

Generate formatted summary of sensitivity analysis results.

Returns:: Formatted summary.
Return type:: str

print_summary()[source]

Print summary to stdout.

Return type:: None

to_dict()[source]

Convert results to dictionary.

Return type:: Dict[str, Any]

to_dataframe()[source]

Convert results to DataFrame.

Return type:: DataFrame

__init__(lb, ub, ci_lb, ci_ub, M, method, original_estimate, original_se, alpha=0.05, ci_method='FLCI', original_results=None, event_study_bounds=None)

Parameters:

lb (float)
ub (float)
ci_lb (float)
ci_ub (float)
M (float)
method (str)
original_estimate (float)
original_se (float)
alpha (float)
ci_method (str)
original_results (Any | None)
event_study_bounds (Dict[Any, Dict[str, float]] | None)

Return type:

None