diff_diff.SyntheticDiDResults

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

__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

Methods

__init__(att, se, t_stat, p_value, conf_int, ...)
get_time_weights_df()	Get time weights as a pandas DataFrame.
get_unit_weights_df()	Get unit weights as a pandas 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
is_significant	Check if the ATT is statistically significant at the alpha level.
n_bootstrap
noise_level
placebo_effects
pre_treatment_fit
significance_stars	Return significance stars based on p-value.
variance_method
zeta_lambda
zeta_omega
att
se
t_stat
p_value
conf_int
n_obs
n_treated
n_control
unit_weights
time_weights
pre_periods
post_periods

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