diff_diff.lpdid_results.LPDiDResults#

class diff_diff.lpdid_results.LPDiDResults[source]#

Bases: object

Results container for the LPDiD estimator.

Holds the per-horizon event_study table and the pooled pre/post table (each a pandas.DataFrame with coefficient, se, t_stat, p_value, conf_low, conf_high, n_obs, n_clusters columns). The headline ATT is the pooled post row.

n_control_units counts never-treated units only (the library-wide field convention, surfaced as “Never-treated units” in summary()); under control_group="clean" the realized control pool at each horizon also includes not-yet-treated cohorts, whose per-horizon counts live in the n_obs / n_clusters columns of the tables.

Methods

`__init__`(event_study, pooled, n_obs, ...[, ...])
`print_summary`()
`summary`()
`to_dataframe`([level])
`to_dict`()

Attributes

`absorb`
`alpha`
`att`
`cluster_name`
`conf_int`
`covariates`
`dylags`
`estimand`
`n_clusters`
`p_value`
`rank_deficient_action`
`se`
`t_stat`
`vcov_type`
`ylags`
`event_study`
`pooled`
`n_obs`
`n_treated_units`
`n_control_units`
`pre_window`
`post_window`
`control_group`
`reweight`
`no_composition`
`pmd`

__init__(event_study, pooled, n_obs, n_treated_units, n_control_units, pre_window, post_window, control_group, reweight, no_composition, pmd, alpha=0.05, cluster_name=None, n_clusters=None, vcov_type='hc1', rank_deficient_action='warn', covariates=None, absorb=None, ylags=0, dylags=0)#

Parameters:

event_study (DataFrame | None)
pooled (DataFrame | None)
n_obs (int)
n_treated_units (int)
n_control_units (int)
pre_window (int)
post_window (int)
control_group (str)
reweight (bool)
no_composition (bool)
pmd (str | int | None)
alpha (float)
cluster_name (str | None)
n_clusters (int | None)
vcov_type (str)
rank_deficient_action (str)
covariates (List[str] | None)
absorb (List[str] | None)
ylags (int)
dylags (int)

Return type:

None

classmethod __new__(*args, **kwargs)#