diff_diff.lwdid_results.LWDiDResults#

class diff_diff.lwdid_results.LWDiDResults[source]#

Bases: BaseResults, AggregationMixin

Results from LWDiD.fit().

Follows the diff-diff standard results interface. Holds the headline ATT estimate and inference for the common-timing case, or per-cohort effects and an overall weighted ATT for the staggered case.

Parameters:

att (float) – Average treatment effect on the treated.
se (float) – Standard error of the ATT estimate.
t_stat (float) – t-statistic (att / se).
p_value (float) – Two-sided p-value.
conf_int (tuple of float) – (lower, upper) confidence interval at level 1 - alpha.
n_obs (int) – Total observations used in estimation.
n_treated (int) – Number of treated units.
n_control (int) – Number of control units.
rolling (str) – Transformation method used (‘demean’, ‘detrend’, ‘demeanq’, or ‘detrendq’).
estimation_method (str) – Estimation method (‘reg’, ‘ipw’, ‘dr’, or ‘psm’).
vcov_type (str) – Variance family (‘classical’, ‘hc1’, ‘hc2’, or ‘hc3’).
alpha (float) – Significance level used for confidence intervals.
df_inference (int or None) – Degrees of freedom used for t-distribution inference.
cluster_name (str or None) – Name of the cluster variable, if clustered.
n_clusters (int or None) – Number of clusters, if clustered.
cohort_effects (dict or None) – Per-cohort ATT results for staggered designs.
params (ndarray or None) – All coefficient estimates from the regression.
bse (ndarray or None) – All standard errors from the regression.
vcov (ndarray or None) – Variance-covariance matrix.

Methods

`__init__`(att, se, t_stat, p_value, conf_int, ...)
`aggregate`(type[, weights, balance_e])	Re-aggregate this fit without refitting.
`print_summary`()	Print the formatted summary to stdout.
`randomization_test`(*[, n_reps, method, seed])	Run Fisher randomization inference on the fitted estimation sample.
`summary`()	Formatted text summary of results.
`to_csv`(path)	Export results to CSV file.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a JSON-serializable dictionary.
`wild_cluster_bootstrap`(*[, n_bootstrap, ...])	Run wild cluster bootstrap inference on the fitted estimation sample.

Attributes

`att_tau_omega_complete_case`	Complete-case tau_omega composite point, exposed as a diagnostic when complete-case drops prevented it from being `.att` (None otherwise).
`bootstrap_pvalue`	Wild cluster bootstrap p-value (None if not computed).
`bse`
`cband_crit_value`
`cband_method`
`cband_n_bootstrap`
`ci`	Alias for conf_int (diff-diff API convention).
`cluster_name`
`cohort_effects`
`cohort_time_effects`
`control_group`
`df_inference`
`event_study_df`
`event_study_effects`
`event_study_vcov`
`event_study_vcov_index`
`inference_basis`
`is_staggered`	Whether this result comes from a staggered adoption design.
`n_bootstrap`
`n_clusters`
`n_composite_controls_dropped`
`n_composite_treated_dropped`	Treated / control units dropped by the tau_omega complete-case resolution (0 when the composite path did not run or dropped none).
`params`
`pscore_trim`	Propensity-score trim bound used by the ipw/dr/psm paths (None for estimation_method='reg', where no propensity model is fitted).
`psm_config`	PSM matching settings (None unless estimation_method='psm'): {'pscore_trim', 'n_neighbors', 'caliper', 'with_replacement'}
`pvalue`	Alias for p_value (diff-diff API convention).
`ri_pvalue`	Randomization inference p-value (None if not computed).
`seed`
`vcov`
`att`
`se`
`t_stat`
`p_value`
`conf_int`
`n_obs`
`n_treated`
`n_control`
`rolling`
`estimation_method`
`vcov_type`
`alpha`
`reference_periods`

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated, n_control, rolling, estimation_method, vcov_type, alpha, df_inference=None, cluster_name=None, n_clusters=None, control_group=None, n_bootstrap=0, seed=None, pscore_trim=None, psm_config=None, cohort_effects=None, cohort_time_effects=None, inference_basis=None, att_tau_omega_complete_case=None, n_composite_treated_dropped=0, n_composite_controls_dropped=0, event_study_effects=None, event_study_vcov=None, event_study_vcov_index=None, event_study_df=None, reference_periods=<factory>, cband_method=None, cband_crit_value=None, cband_n_bootstrap=None, params=None, bse=None, vcov=None, _ri_result=None, _wcb_result=None)#

Parameters:

att (float)
se (float)
t_stat (float)
p_value (float)
conf_int (Tuple[float, float])
n_obs (int)
n_treated (int)
n_control (int)
rolling (str)
estimation_method (str)
vcov_type (str)
alpha (float)
df_inference (int | None)
cluster_name (str | None)
n_clusters (int | None)
control_group (str | None)
n_bootstrap (int)
seed (int | None)
pscore_trim (float | None)
psm_config (Dict[str, Any] | None)
cohort_effects (Dict[Any, Dict] | None)
cohort_time_effects (Dict[Tuple[Any, Any], Dict] | None)
inference_basis (str | None)
att_tau_omega_complete_case (float | None)
n_composite_treated_dropped (int)
n_composite_controls_dropped (int)
event_study_effects (Dict[int, Dict] | None)
event_study_vcov (ndarray | None)
event_study_vcov_index (ndarray | None)
event_study_df (Dict[int, float] | None)
reference_periods (Tuple[int, ...])
cband_method (str | None)
cband_crit_value (float | None)
cband_n_bootstrap (int | None)
params (ndarray | None)
bse (ndarray | None)
vcov (ndarray | None)
_ri_result (Any | None)
_wcb_result (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#