panel_data_did

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Bases: pydantic.BaseModel

Validated long-format panel contract for staggered-adoption DID.

Required fields

df : pandas.DataFrame Long-format panel data. y : str Outcome column name in df. unit_col : str Unit identifier column name in df. time_col : str Calendar time column name in df. Preferred input format is pandas.Period values with a regular frequency (for example monthly Period['M']). Datetime/string values are accepted only when a regular frequency can be inferred. treated_time : str Binary period-level treatment column in df (0/1 or False/True). Treatment is required to be absorbing: once a unit is treated, every later observed row for that unit must remain treated. covariates : sequence of str, optional Optional numeric covariate column names in df. The input alias covariants is accepted for convenience. cluster_col : str, optional Optional cluster identifier column name in df. The input alias cluster is accepted for convenience. unit_col is valid for unit-level clustering. Estimators that form unit-level influence functions require non-unit cluster columns to be stable within unit; time_col is therefore valid for the contract but not for those estimators.

Notes

Extra keyword arguments are rejected. The contract derives treated_units, control_units (never-treated units), cohorts, per-unit first treatment dates, and time_freq from the input data. treatment_start is kept as a compatibility alias for the earliest treatment cohort; staggered estimators should use cohorts and first_treatment_by_unit instead. This contract is intended for Callaway & Sant’Anna-style staggered difference-in-differences designs. It validates cohort support for at least one estimable ATT(g,t) cell using not-yet-treated or never-treated comparison units and exposes a full support table via att_gt_cells. The model stores a validated internal dataframe snapshot used by all contract methods; mutating the public df attribute after construction does not affect validated contract behavior. Outcome y must not contain null/NaN values. Represent missing panel periods by omitting unit-time rows, not by keeping rows with NaN outcome. For fiscal quarter/year semantics, pass time_col explicitly as pandas.Period with the desired fiscal frequency.

Initialization

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

‘ConfigDict(…)’

‘Field(…)’

‘Field(…)’

‘Field(…)’

‘Field(…)’

‘Field(…)’

‘Field(…)’

‘Field(…)’

Units that are never treated in the observed panel.

Map every unit to its first treatment period, or None if never treated.

Map ever-treated units to their first treatment cohort.

Sorted first-treatment periods among ever-treated units.

Return a copy of the validated covariate design columns.

Return a copy of the validated cluster identifier column.

Return units first treated in the requested cohort.

Return units untreated at time because they adopt later or never adopt.

Return valid comparison units for a Callaway-Sant’Anna ATT(g,t) cell.

Return analysis data with derived staggered-DID cohort and event-time columns.

Return Callaway-Sant’Anna ATT(g,t) support under an explicit policy.

Return ever-treated/never-treated row counts by analysis period and own-treatment status.