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

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.
`df_survey`
`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`
`post_periods_used`
`pre_periods_used`
`significance_stars`	Return significance indicator if robust CI excludes zero.
`survey_metadata`
`target_label`
`lb`
`ub`
`ci_lb`
`ci_ub`
`M`
`method`
`original_estimate`
`original_se`

__init__(lb, ub, ci_lb, ci_ub, M, method, original_estimate, original_se, alpha=0.05, ci_method='FLCI', target_label='Equal-weight avg over post horizons', pre_periods_used=None, post_periods_used=None, original_results=None, event_study_bounds=None, survey_metadata=None, df_survey=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)
target_label (str)
pre_periods_used (List[Any] | None)
post_periods_used (List[Any] | None)
original_results (Any | None)
event_study_bounds (Dict[Any, Dict[str, float]] | None)
survey_metadata (Any | None)
df_survey (int | None)

Return type:

None

classmethod __new__(*args, **kwargs)#