Aggregate the ATTgt

Parameters:¶

type_of_aggregation: str | None = 'simple'¶

• "simple"

  to calculate the weighted average of all cohort-time average treatment effects, with weights proportional to the cohort size.

• "event" or "event"

  to calculate the average effects in each relative period: periods relative to the treatment; as in an event study.

• "cohort"

  to calculate the average treatment effect in each cohort.

• "time" or "time"

  to calculate the average treatment effect in each time time.

overall: bool = False¶

calculates the average effect within each type_of_aggregation.

• if type_of_aggregation is set to "event" or "event"

  to calculate the average effect of the treatment across positive relative periods

• if type_of_aggregation is set to "cohort"

  to calculate the average effect of the treatment across cohorts

• if type_of_aggregation is set to "time" or "time"

  to calculate the average effect of the treatment across time times

difference: bool | list | dict[str, list] = False¶

take the difference of the estimates

Available options are:

• True

  to calculate the difference between 2 samples or 2 strata of treatments

Note

• Samples difference: if the estimation is run on 2 samples and more than 2 strata, the estimates for the two samples will be subtracted, as long as there are no strata that have the same names as the samples, in that case use a dictionary as indicated below

• strata difference: if the estimation is run on 2 strata and more than 2 samples, the estimates for the two strata will be subtracted, as long as there are no samples that have the same names as the strata, in that case use a dictionary as indicated below

• [sample-0, sample-1] or [stratum-A, stratum-B]

  to calculate the difference between 2 samples listed in the argument or the 2 strata of treatments listed in the argument

Note

• Samples difference: if there are strata with the same name as the two samples listed, use a dictionary as indicated below

• strata difference: if there are samples with the same name as the two strata listed, use a dictionary as indicated below

• {'strata': [stratum-A, stratum-B]} or {'sample_names': [sample-0, sample-1]}

alpha: float = 0.05¶

The significance level.

cluster_var: list | str = None¶

cluster variables

boot_iterations: int = 0¶

bootstrap iterations

random_state: int = None¶

seed for bootstrap

n_jobs: int = 1¶

The maximum number of concurrently running jobs. If -1 all CPUs are used.

If ≠ 1, concurrent jobs will be run for:

• computing the bootstrap; the influence function is split into n_jobs parts and the boostrap is computed concurrently for each part

Parallelization is implemented using joblib, refer to its documentation for additional details on n_jobs.

backend: str = 'loky'¶

Parallelization backend implementation.

Parallelization is implemented using joblib, refer to its documentation for additional details on backend.

Return type:¶

A DataFrame with the requested aggregation