diff_diff.AggregationResult#

class diff_diff.AggregationResult[source]#

Bases: BaseResults

One post-fit aggregation, as a table (spec section 6, row M-122).

Columnar arrays index-aligned to label. Values are computed by the producing estimator’s aggregation machinery and stored here verbatim - this container never re-derives inference.

Parameters:

  • level (str) – The aggregation type that produced this table - one of AGGREGATION_VOCABULARY or a documented per-estimator extra.

  • label (np.ndarray) – Per-row aggregation key: the cohort for "group", the calendar period for "calendar", the dose for "dose". A single "overall" entry for "simple"; a single "total" entry for "total".

  • target (np.ndarray) – Per-row estimand discriminator, so one container can carry two aligned estimands over the same labels (ContinuousDiD’s ATT(d) and ACRT(d) become 2N rows). "att" where an estimator has one; "total" on a "total" row - a total incremental outcome over the estimator’s aggregation mass, not an ATT.

  • att (np.ndarray) – The canonical quintet, per row, carrying WHATEVER inference the fit stored - never recomputed. On a bootstrapped fit that usually means the producer’s percentile-bootstrap statistics carried through unchanged; view-relay producers can mix regimes per row where the fit itself did (dCDH’s L_max >= 2 cost-benefit delta keeps analytical safe_inference even under n_bootstrap > 0 - see the REGISTRY Phase 2 cost-benefit delta SE note).

  • se (np.ndarray) – The canonical quintet, per row, carrying WHATEVER inference the fit stored - never recomputed. On a bootstrapped fit that usually means the producer’s percentile-bootstrap statistics carried through unchanged; view-relay producers can mix regimes per row where the fit itself did (dCDH’s L_max >= 2 cost-benefit delta keeps analytical safe_inference even under n_bootstrap > 0 - see the REGISTRY Phase 2 cost-benefit delta SE note).

  • t_stat (np.ndarray) – The canonical quintet, per row, carrying WHATEVER inference the fit stored - never recomputed. On a bootstrapped fit that usually means the producer’s percentile-bootstrap statistics carried through unchanged; view-relay producers can mix regimes per row where the fit itself did (dCDH’s L_max >= 2 cost-benefit delta keeps analytical safe_inference even under n_bootstrap > 0 - see the REGISTRY Phase 2 cost-benefit delta SE note).

  • p_value (np.ndarray) – The canonical quintet, per row, carrying WHATEVER inference the fit stored - never recomputed. On a bootstrapped fit that usually means the producer’s percentile-bootstrap statistics carried through unchanged; view-relay producers can mix regimes per row where the fit itself did (dCDH’s L_max >= 2 cost-benefit delta keeps analytical safe_inference even under n_bootstrap > 0 - see the REGISTRY Phase 2 cost-benefit delta SE note).

  • conf_int_lower (np.ndarray) – Interval bounds at the fit’s alpha.

  • conf_int_upper (np.ndarray) – Interval bounds at the fit’s alpha.

  • n (np.ndarray) – Per-row count as float, NaN where the producer records none. Its SEMANTIC is n_kind - never assume units.

  • n_kind (str or None) – Semantic of n, from N_KIND_VOCABULARY - the SAME closed vocabulary EventStudyResults draws on, so a consumer can route on n_kind across both containers. None when the producer records no count.

  • weight (np.ndarray or None) – Normalized aggregation mass per row, summing to 1 within one (level, target) group. None where no per-row mass exists - CallawaySantAnna’s "group" aggregation weights (g, t) cells equally WITHIN each cohort and has no cross-cohort mass, so inventing one would be a fabricated number. Single-row containers ("simple", "total") carry weight=[1.0].

  • df (np.ndarray) – Per-row inference degrees of freedom, NaN where none governed the stored p-value. NaN on percentile-bootstrap rows (no df governs them); a bootstrapped fit’s rows can still carry a finite df where the fit kept analytical inference for that row (the dCDH delta case above).

  • alpha (float) – Significance level the interval was computed at.

  • estimator (str or None) – Producing estimator class name, for provenance in summary().

Methods

__init__(level, label, target, att, se, ...)

summary([alpha])

Human-readable table.

to_dataframe()

Return the pinned AGGREGATION_SCHEMA columns, in order.

to_dict()

Canonical-name mapping (deprecated names never leak into output).

Attributes

alpha

estimator

n_kind

weight

level

label

target

att

se

t_stat

p_value

conf_int_lower

conf_int_upper

n

df

__init__(level, label, target, att, se, t_stat, p_value, conf_int_lower, conf_int_upper, n, df, alpha=0.05, n_kind=None, weight=None, estimator=None, _COLUMN_FIELDS=('label', 'target', 'att', 'se', 't_stat', 'p_value', 'conf_int_lower', 'conf_int_upper', 'n', 'df'))#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#