Results Classes

Dataclass containers for estimation results from various estimators.

DiDResults

Results from basic DifferenceInDifferences estimation.

class diff_diff.DiDResults

Bases: object

Results from a Difference-in-Differences estimation.

Provides easy access to coefficients, standard errors, confidence intervals, and summary statistics in a Pythonic way.

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

Attributes

att
se
t_stat
p_value
n_obs
is_significant	Check if the ATT is statistically significant at the alpha level.
significance_stars	Return significance stars based on p-value.

Methods

summary([alpha])	Generate a formatted summary of the estimation results.
to_dict()	Convert results to a dictionary.
to_dataframe()	Convert results to a pandas DataFrame.

att: float

se: float

t_stat: float

p_value: float

conf_int: Tuple[float, float]

n_obs: int

n_treated: int

n_control: int

alpha: float = 0.05

coefficients: Dict[str, float] | None = None

vcov: ndarray | None = None

residuals: ndarray | None = None

fitted_values: ndarray | None = None

r_squared: float | None = None

inference_method: str = 'analytical'

n_bootstrap: int | None = None

n_clusters: int | None = None

bootstrap_distribution: ndarray | None = None

__repr__()

Concise string representation.

Return type:

str

summary(alpha=None)

Generate a formatted summary of the estimation results.

Parameters:

alpha (float, optional) – Significance level for confidence intervals. Defaults to the alpha used during estimation.

Returns:

Formatted summary table.

Return type:

str

print_summary(alpha=None)

Print the summary to stdout.

Parameters:

alpha (float | None)

Return type:

None

to_dict()

Convert results to a dictionary.

Returns:

Dictionary containing all estimation results.

Return type:

Dict[str, Any]

to_dataframe()

Convert results to a pandas DataFrame.

Returns:

DataFrame with estimation results.

Return type:

pd.DataFrame

property is_significant: bool

Check if the ATT is statistically significant at the alpha level.

property significance_stars: str

Return significance stars based on p-value.

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated, n_control, alpha=0.05, coefficients=None, vcov=None, residuals=None, fitted_values=None, r_squared=None, inference_method='analytical', n_bootstrap=None, n_clusters=None, bootstrap_distribution=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)

• alpha (float)

• coefficients (Dict[str, float] | None)

• vcov (ndarray | None)

• residuals (ndarray | None)

• fitted_values (ndarray | None)

• r_squared (float | None)

• inference_method (str)

• n_bootstrap (int | None)

• n_clusters (int | None)

• bootstrap_distribution (ndarray | None)

Return type:

None

MultiPeriodDiDResults

Results from MultiPeriodDiD event study estimation.

class diff_diff.MultiPeriodDiDResults

Bases: object

Results from a Multi-Period Difference-in-Differences estimation.

Provides access to period-specific treatment effects as well as an aggregate average treatment effect.

period_effects

Dictionary mapping period identifiers to their PeriodEffect objects. Contains all estimated period effects (pre and post, excluding the reference period which is normalized to zero).

Type:

dict[any, PeriodEffect]

avg_att

Average Treatment effect on the Treated across post-periods only.

Type:

float

avg_se

Standard error of the average ATT.

Type:

float

avg_t_stat

T-statistic for the average ATT.

Type:

float

avg_p_value

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

Type:

float

avg_conf_int

Confidence interval for the average ATT.

Type:

tuple[float, float]

n_obs

Number of observations used in estimation.

Type:

int

n_treated

Number of treated observations.

Type:

int

n_control

Number of control observations.

Type:

int

pre_periods

List of pre-treatment period identifiers.

Type:

list

post_periods

List of post-treatment period identifiers.

Type:

list

reference_period

The reference (omitted) period. Its coefficient is zero by construction and it is excluded from period_effects.

Type:

any, optional

interaction_indices

Mapping from period identifier to column index in the full variance-covariance matrix. Used internally for sub-VCV extraction (e.g., by HonestDiD and PreTrendsPower).

Type:

dict, optional

Attributes

period_effects
att
pre_periods
post_periods
reference_period
interaction_indices
pre_period_effects	Pre-period effects only (for parallel trends assessment).
post_period_effects	Post-period effects only.

period_effects: Dict[Any, PeriodEffect]

avg_att: float

avg_se: float

avg_t_stat: float

avg_p_value: float

avg_conf_int: Tuple[float, float]

n_obs: int

n_treated: int

n_control: int

pre_periods: List[Any]

post_periods: List[Any]

alpha: float = 0.05

coefficients: Dict[str, float] | None = None

vcov: ndarray | None = None

residuals: ndarray | None = None

fitted_values: ndarray | None = None

r_squared: float | None = None

reference_period: Any | None = None

interaction_indices: Dict[Any, int] | None = None

__repr__()

Concise string representation.

Return type:

str

property pre_period_effects: Dict[Any, PeriodEffect]

Pre-period effects only (for parallel trends assessment).

property post_period_effects: Dict[Any, PeriodEffect]

Post-period effects only.

summary(alpha=None)

Generate a formatted summary of the estimation results.

Parameters:

alpha (float, optional) – Significance level for confidence intervals. Defaults to the alpha used during estimation.

Returns:

Formatted summary table.

Return type:

str

print_summary(alpha=None)

Print the summary to stdout.

Parameters:

alpha (float | None)

Return type:

None

get_effect(period)

Get the treatment effect for a specific period.

Parameters:

period (any) – The period identifier.

Returns:

The treatment effect for the specified period.

Return type:

PeriodEffect

Raises:

KeyError – If the period is not found in post-treatment periods.

to_dict()

Convert results to a dictionary.

Returns:

Dictionary containing all estimation results.

Return type:

Dict[str, Any]

to_dataframe()

Convert period-specific effects to a pandas DataFrame.

Returns:

DataFrame with one row per estimated period (pre and post).

Return type:

pd.DataFrame

property is_significant: bool

Check if the average ATT is statistically significant at the alpha level.

property significance_stars: str

Return significance stars for the average ATT based on p-value.

__init__(period_effects, avg_att, avg_se, avg_t_stat, avg_p_value, avg_conf_int, n_obs, n_treated, n_control, pre_periods, post_periods, alpha=0.05, coefficients=None, vcov=None, residuals=None, fitted_values=None, r_squared=None, reference_period=None, interaction_indices=None)

Parameters:

• period_effects (Dict[Any, PeriodEffect])

• avg_att (float)

• avg_se (float)

• avg_t_stat (float)

• avg_p_value (float)

• avg_conf_int (Tuple[float, float])

• n_obs (int)

• n_treated (int)

• n_control (int)

• pre_periods (List[Any])

• post_periods (List[Any])

• alpha (float)

• coefficients (Dict[str, float] | None)

• vcov (ndarray | None)

• residuals (ndarray | None)

• fitted_values (ndarray | None)

• r_squared (float | None)

• reference_period (Any | None)

• interaction_indices (Dict[Any, int] | None)

Return type:

None

PeriodEffect

Container for a single period’s treatment effect in event studies.

class diff_diff.PeriodEffect

Bases: object

Treatment effect for a single time period.

period

The time period identifier.

Type:

any

effect

The treatment effect estimate for this period.

Type:

float

se

Standard error of the effect estimate.

Type:

float

t_stat

T-statistic for the effect estimate.

Type:

float

p_value

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

Type:

float

conf_int

Confidence interval for the effect.

Type:

tuple[float, float]

period: Any

effect: float

se: float

t_stat: float

p_value: float

conf_int: Tuple[float, float]

__repr__()

Concise string representation.

Return type:

str

property is_significant: bool

Check if the effect is statistically significant at 0.05 level.

property significance_stars: str

Return significance stars based on p-value.

__init__(period, effect, se, t_stat, p_value, conf_int)

Parameters:

• period (Any)

• effect (float)

• se (float)

• t_stat (float)

• p_value (float)

• conf_int (Tuple[float, float])

Return type:

None

SyntheticDiDResults

Results from SyntheticDiD estimation.

class diff_diff.SyntheticDiDResults

Bases: object

Results from a Synthetic Difference-in-Differences estimation.

Combines DiD with synthetic control by re-weighting control units to match pre-treatment trends of treated units.

att

Average Treatment effect on the Treated (ATT).

Type:

float

se

Standard error of the ATT estimate (bootstrap or placebo-based).

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

unit_weights

Dictionary mapping control unit IDs to their synthetic weights.

Type:

dict

time_weights

Dictionary mapping pre-treatment periods to their time weights.

Type:

dict

pre_periods

List of pre-treatment period identifiers.

Type:

list

post_periods

List of post-treatment period identifiers.

Type:

list

variance_method

Method used for variance estimation: “bootstrap” or “placebo”.

Type:

str

Attributes

att
unit_weights
time_weights

att: float

se: float

t_stat: float

p_value: float

conf_int: Tuple[float, float]

n_obs: int

n_treated: int

n_control: int

unit_weights: Dict[Any, float]

time_weights: Dict[Any, float]

pre_periods: List[Any]

post_periods: List[Any]

alpha: float = 0.05

variance_method: str = 'placebo'

noise_level: float | None = None

zeta_omega: float | None = None

zeta_lambda: float | None = None

pre_treatment_fit: float | None = None

placebo_effects: ndarray | None = None

n_bootstrap: int | None = None

__repr__()

Concise string representation.

Return type:

str

summary(alpha=None)

Generate a formatted summary of the estimation results.

Parameters:

alpha (float, optional) – Significance level for confidence intervals. Defaults to the alpha used during estimation.

Returns:

Formatted summary table.

Return type:

str

print_summary(alpha=None)

Print the summary to stdout.

Parameters:

alpha (float | None)

Return type:

None

to_dict()

Convert results to a dictionary.

Returns:

Dictionary containing all estimation results.

Return type:

Dict[str, Any]

to_dataframe()

Convert results to a pandas DataFrame.

Returns:

DataFrame with estimation results.

Return type:

pd.DataFrame

get_unit_weights_df()

Get unit weights as a pandas DataFrame.

Returns:

DataFrame with unit IDs and their weights.

Return type:

pd.DataFrame

get_time_weights_df()

Get time weights as a pandas DataFrame.

Returns:

DataFrame with time periods and their weights.

Return type:

pd.DataFrame

property is_significant: bool

Check if the ATT is statistically significant at the alpha level.

property significance_stars: str

Return significance stars based on p-value.

__init__(att, se, t_stat, p_value, conf_int, n_obs, n_treated, n_control, unit_weights, time_weights, pre_periods, post_periods, alpha=0.05, variance_method='placebo', noise_level=None, zeta_omega=None, zeta_lambda=None, pre_treatment_fit=None, placebo_effects=None, n_bootstrap=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)

• unit_weights (Dict[Any, float])

• time_weights (Dict[Any, float])

• pre_periods (List[Any])

• post_periods (List[Any])

• alpha (float)

• variance_method (str)

• noise_level (float | None)

• zeta_omega (float | None)

• zeta_lambda (float | None)

• pre_treatment_fit (float | None)

• placebo_effects (ndarray | None)

• n_bootstrap (int | None)

Return type:

None