diff_diff.TROPResults#

class diff_diff.TROPResults[source]#

Bases: object

Results from a Triply Robust Panel (TROP) estimation.

TROP combines nuclear norm regularized factor estimation with exponential distance-based unit weights and time decay weights.

att#

Average Treatment effect on the Treated (ATT).

Type:: float

se#

Standard error of the ATT estimate.

Type:: float

t_stat#

T-statistic for the ATT estimate.

Type:: float

p_value#

P-value for the null hypothesis that ATT = 0.

Type:: float

conf_int#

Confidence interval for the ATT.

Type:: tuple[float, float]

n_obs#

Number of observations used in estimation.

Type:: int

n_treated#

Number of treated units.

Type:: int

n_control#

Number of control units.

Type:: int

n_treated_obs#

Number of treated unit-time observations.

Type:: int

unit_effects#

Estimated unit fixed effects (alpha_i).

Type:: dict

time_effects#

Estimated time fixed effects (beta_t).

Type:: dict

treatment_effects#

Individual treatment effects for each treated (unit, time) pair.

Type:: dict

lambda_time#

Selected time weight decay parameter from grid. 0.0 = uniform time weights (disabled) per Eq. 3.

Type:: float

lambda_unit#

Selected unit weight decay parameter from grid. 0.0 = uniform unit weights (disabled) per Eq. 3.

Type:: float

lambda_nn#

Selected nuclear norm regularization parameter from grid. inf = factor model disabled (L=0); converted to 1e10 internally for computation.

Type:: float

factor_matrix#

Estimated low-rank factor matrix L (n_periods x n_units).

Type:: np.ndarray

effective_rank#

Effective rank of the factor matrix (sum of singular values / max).

Type:: float

loocv_score#

Leave-one-out cross-validation score for selected parameters.

Type:: float

alpha#

Significance level for confidence interval.

Type:: float

n_pre_periods#

Number of pre-treatment periods.

Type:: int

n_post_periods#

Number of post-treatment periods (periods with D=1 observations).

Type:: int

n_bootstrap#

Number of bootstrap replications (if bootstrap variance).

Type:: int, optional

bootstrap_distribution#

Bootstrap distribution of estimates.

Type:: np.ndarray, optional

Methods

`__init__`(att, se, t_stat, p_value, conf_int, ...)
`get_time_effects_df`()	Get time fixed effects as a DataFrame.
`get_treatment_effects_df`()	Get individual treatment effects as a DataFrame.
`get_unit_effects_df`()	Get unit fixed effects as a DataFrame.
`print_summary`([alpha])	Print the summary to stdout.
`summary`([alpha])	Generate a formatted summary of the estimation results.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`alpha`
`bootstrap_distribution`
`coef_var`	SE / abs(ATT).
`is_significant`	Check if the ATT is statistically significant at the alpha level.
`n_bootstrap`
`n_post_periods`
`n_pre_periods`
`significance_stars`	Return significance stars based on p-value.
`survey_metadata`
`att`
`se`
`t_stat`
`p_value`
`conf_int`
`n_obs`
`n_treated`
`n_control`
`n_treated_obs`
`unit_effects`
`time_effects`
`treatment_effects`
`lambda_time`
`lambda_unit`
`lambda_nn`
`factor_matrix`
`effective_rank`
`loocv_score`

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated, n_control, n_treated_obs, unit_effects, time_effects, treatment_effects, lambda_time, lambda_unit, lambda_nn, factor_matrix, effective_rank, loocv_score, alpha=0.05, n_pre_periods=0, n_post_periods=0, n_bootstrap=None, bootstrap_distribution=None, survey_metadata=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)
n_treated_obs (int)
unit_effects (Dict[Any, float])
time_effects (Dict[Any, float])
treatment_effects (Dict[Tuple[Any, Any], float])
lambda_time (float)
lambda_unit (float)
lambda_nn (float)
factor_matrix (ndarray)
effective_rank (float)
loocv_score (float)
alpha (float)
n_pre_periods (int)
n_post_periods (int)
n_bootstrap (int | None)
bootstrap_distribution (ndarray | None)
survey_metadata (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#