diff_diff.PlaceboTestResults

class diff_diff.PlaceboTestResults[source]

Bases: object

Results from a placebo test for DiD assumption validation.

test_type

Type of placebo test performed.

Type:: str

placebo_effect

Estimated placebo treatment effect.

Type:: float

se

Standard error of the placebo effect.

Type:: float

t_stat

T-statistic for the placebo effect.

Type:: float

p_value

P-value for testing placebo_effect = 0.

Type:: float

conf_int

Confidence interval for the placebo effect.

Type:: tuple

n_obs

Number of observations used in the test.

Type:: int

is_significant

Whether the placebo effect is significant at alpha=0.05.

Type:: bool

original_effect

Original ATT estimate for comparison.

Type:: float, optional

original_se

Original SE for comparison.

Type:: float, optional

permutation_distribution

Distribution of permuted effects (for permutation test).

Type:: np.ndarray, optional

leave_one_out_effects

Unit-specific effects (for leave-one-out test).

Type:: dict, optional

fake_period

The fake treatment period used (for timing test).

Type:: any, optional

fake_group

The fake treatment group used (for group test).

Type:: list, optional

__init__(test_type, placebo_effect, se, t_stat, p_value, conf_int, n_obs, is_significant, alpha=0.05, original_effect=None, original_se=None, permutation_distribution=None, leave_one_out_effects=None, fake_period=None, fake_group=None, n_permutations=None)

Parameters:

test_type (str)
placebo_effect (float)
se (float)
t_stat (float)
p_value (float)
conf_int (Tuple[float, float])
n_obs (int)
is_significant (bool)
alpha (float)
original_effect (float | None)
original_se (float | None)
permutation_distribution (ndarray | None)
leave_one_out_effects (Dict[Any, float] | None)
fake_period (Any | None)
fake_group (List[Any] | None)
n_permutations (int | None)

Return type:

None

Methods

`__init__`(test_type, placebo_effect, se, ...)
`print_summary`()	Print summary to stdout.
`summary`()	Generate formatted summary of placebo test results.
`to_dataframe`()	Convert results to a DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`alpha`
`fake_group`
`fake_period`
`leave_one_out_effects`
`n_permutations`
`original_effect`
`original_se`
`permutation_distribution`
`significance_stars`	Return significance stars based on p-value.
`test_type`
`placebo_effect`
`se`
`t_stat`
`p_value`
`conf_int`
`n_obs`
`is_significant`

test_type: str

placebo_effect: float

se: float

t_stat: float

p_value: float

conf_int: Tuple[float, float]

n_obs: int

is_significant: bool

alpha: float = 0.05

original_effect: float | None = None

original_se: float | None = None

permutation_distribution: ndarray | None = None

leave_one_out_effects: Dict[Any, float] | None = None

fake_period: Any | None = None

fake_group: List[Any] | None = None

n_permutations: int | None = None

property significance_stars: str: Return significance stars based on p-value.

summary()[source]

Generate formatted summary of placebo test results.

Return type:: str

print_summary()[source]

Print summary to stdout.

Return type:: None

to_dict()[source]

Convert results to a dictionary.

Return type:: Dict[str, Any]

to_dataframe()[source]

Convert results to a DataFrame.

Return type:: DataFrame

__init__(test_type, placebo_effect, se, t_stat, p_value, conf_int, n_obs, is_significant, alpha=0.05, original_effect=None, original_se=None, permutation_distribution=None, leave_one_out_effects=None, fake_period=None, fake_group=None, n_permutations=None)

Parameters:

test_type (str)
placebo_effect (float)
se (float)
t_stat (float)
p_value (float)
conf_int (Tuple[float, float])
n_obs (int)
is_significant (bool)
alpha (float)
original_effect (float | None)
original_se (float | None)
permutation_distribution (ndarray | None)
leave_one_out_effects (Dict[Any, float] | None)
fake_period (Any | None)
fake_group (List[Any] | None)
n_permutations (int | None)

Return type:

None