diff-diff/diff_diff/efficient_did_bootstrap.py at main · UtilityTools/diff-diff · GitHub

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"""

Multiplier bootstrap inference for the Efficient DiD estimator.

Pattern follows CallawaySantAnnaBootstrapMixin (staggered_bootstrap.py).

Perturbs EIF values with random weights to obtain bootstrap distributions

of ATT(g,t) and aggregated parameters.

"""

import warnings

from dataclasses import dataclass, field

from typing import Any, Dict, List, Optional, Tuple

import numpy as np

from diff_diff.bootstrap_utils import (

compute_effect_bootstrap_stats as _compute_effect_bootstrap_stats_func,

)

from diff_diff.bootstrap_utils import (

generate_bootstrap_weights_batch as _generate_bootstrap_weights_batch,

)

@dataclass

class EDiDBootstrapResults:

"""Bootstrap inference results for EfficientDiD."""

n_bootstrap: int

weight_type: str

alpha: float

overall_att_se: float

overall_att_ci: Tuple[float, float]

overall_att_p_value: float

group_time_ses: Dict[Tuple[Any, Any], float]

group_time_cis: Dict[Tuple[Any, Any], Tuple[float, float]]

group_time_p_values: Dict[Tuple[Any, Any], float]

event_study_ses: Optional[Dict[int, float]] = None

event_study_cis: Optional[Dict[int, Tuple[float, float]]] = None

event_study_p_values: Optional[Dict[int, float]] = None

group_effect_ses: Optional[Dict[Any, float]] = None

group_effect_cis: Optional[Dict[Any, Tuple[float, float]]] = None

group_effect_p_values: Optional[Dict[Any, float]] = None

bootstrap_distribution: Optional[np.ndarray] = field(default=None, repr=False)

class EfficientDiDBootstrapMixin:

"""Mixin providing multiplier bootstrap for EfficientDiD."""

n_bootstrap: int

bootstrap_weights: str

alpha: float

seed: Optional[int]

anticipation: int

def _run_multiplier_bootstrap(

self,

group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]],

eif_by_gt: Dict[Tuple[Any, Any], np.ndarray],

n_units: int,

aggregate: Optional[str],

balance_e: Optional[int],

treatment_groups: List[Any],

cohort_fractions: Dict[float, float],

cluster_indices: Optional[np.ndarray] = None,

n_clusters: Optional[int] = None,

resolved_survey: object = None,

unit_level_weights: Optional[np.ndarray] = None,

) -> EDiDBootstrapResults:

"""Run multiplier bootstrap on stored EIF values.

For each bootstrap draw *b*, perturb ATT(g,t) as::

ATT_b(g,t) = ATT(g,t) + (1/n) * xi_b @ eif_gt

where ``xi_b`` is an i.i.d. weight vector of length ``n_units``.

When ``cluster_indices`` is provided, weights are generated at the

cluster level and expanded to units.

Aggregations (overall, event study, group) are recomputed from

the perturbed ATT(g,t) values.

Note: Bootstrap aggregation uses fixed cohort-size weights, consistent

with the Callaway-Sant'Anna bootstrap pattern (staggered_bootstrap.py).

The analytical path includes a WIF correction for aggregated SEs, but

the bootstrap captures weight uncertainty through EIF perturbation.

This matches the R ``did`` package approach.

"""

if self.n_bootstrap < 50:

warnings.warn(

f"n_bootstrap={self.n_bootstrap} is low. Consider n_bootstrap >= 199 "

"for reliable inference.",

UserWarning,

stacklevel=3,

)

rng = np.random.default_rng(self.seed)

gt_pairs = list(group_time_effects.keys())

n_gt = len(gt_pairs)

# Generate bootstrap weights — PSU-level when survey design is present,

# cluster-level if clustered, unit-level otherwise.

_use_survey_bootstrap = resolved_survey is not None and (

resolved_survey.strata is not None

or resolved_survey.psu is not None

or resolved_survey.fpc is not None

)

if _use_survey_bootstrap:

from diff_diff.bootstrap_utils import (

generate_survey_multiplier_weights_batch as _gen_survey_weights,

)

psu_weights, psu_ids = _gen_survey_weights(

self.n_bootstrap, resolved_survey, self.bootstrap_weights, rng

)

# Build unit -> PSU column map

if resolved_survey.psu is not None:

psu_id_to_col = {int(p): c for c, p in enumerate(psu_ids)}

unit_to_psu_col = np.array(

[psu_id_to_col[int(resolved_survey.psu[i])] for i in range(n_units)]

)

else:

unit_to_psu_col = np.arange(n_units)

all_weights = psu_weights[:, unit_to_psu_col]

elif cluster_indices is not None and n_clusters is not None:

cluster_weights = _generate_bootstrap_weights_batch(

self.n_bootstrap, n_clusters, self.bootstrap_weights, rng

)

# Expand cluster weights to unit level

all_weights = cluster_weights[:, cluster_indices]

else:

all_weights = _generate_bootstrap_weights_batch(

self.n_bootstrap, n_units, self.bootstrap_weights, rng

)

# Original ATTs

original_atts = np.array([group_time_effects[gt]["effect"] for gt in gt_pairs])

# Perturbed ATTs: (n_bootstrap, n_gt)

# Under survey design, perturb survey-score object w_i * eif_i / sum(w)

# to match the analytical variance convention (compute_survey_if_variance).

bootstrap_atts = np.zeros((self.n_bootstrap, n_gt))

for j, gt in enumerate(gt_pairs):

eif_gt = eif_by_gt[gt] # shape (n_units,)

with np.errstate(divide="ignore", invalid="ignore", over="ignore"):

if unit_level_weights is not None:

total_w = float(np.sum(unit_level_weights))

eif_scaled = unit_level_weights * eif_gt / total_w

perturbation = all_weights @ eif_scaled

else:

perturbation = (all_weights @ eif_gt) / n_units

bootstrap_atts[:, j] = original_atts[j] + perturbation

# Post-treatment mask — also exclude NaN effects

post_mask = np.array(

[

t >= g - self.anticipation and np.isfinite(original_atts[j])

for j, (g, t) in enumerate(gt_pairs)

]

)

post_indices = np.where(post_mask)[0]

# Overall ATT: fixed-weight re-aggregation of perturbed cell ATTs.

# This matches CallawaySantAnna._run_multiplier_bootstrap

# (staggered_bootstrap.py:281). The analytical path includes a WIF

# correction; bootstrap captures sampling variability through per-cell

# EIF perturbation without re-estimating weights — this is standard

# in both this library's CS implementation and the R did package.

skip_overall = len(post_indices) == 0

if skip_overall:

bootstrap_overall = np.full(self.n_bootstrap, np.nan)

original_overall = np.nan

else:

post_groups = [gt_pairs[i][0] for i in post_indices]

pg = np.array([cohort_fractions.get(g, 0.0) for g in post_groups])

agg_w = pg / pg.sum() if pg.sum() > 0 else np.ones(len(pg)) / len(pg)

original_overall = float(np.sum(agg_w * original_atts[post_mask]))

with np.errstate(divide="ignore", invalid="ignore", over="ignore"):

bootstrap_overall = bootstrap_atts[:, post_indices] @ agg_w

# Event study: fixed-weight re-aggregation (same pattern as overall).

# See note above re: WIF — analytical WIF is not needed in bootstrap.

bootstrap_event_study = None

event_study_info = None

if aggregate in ("event_study", "all"):

event_study_info = self._prepare_es_agg_boot(

gt_pairs, original_atts, cohort_fractions, balance_e

)

bootstrap_event_study = {}

for e, info in event_study_info.items():

idx = info["gt_indices"]

w = info["weights"]

with np.errstate(divide="ignore", invalid="ignore", over="ignore"):

bootstrap_event_study[e] = bootstrap_atts[:, idx] @ w

# Group aggregation

bootstrap_group = None

group_agg_info = None

if aggregate in ("group", "all"):

group_agg_info = self._prepare_group_agg_boot(gt_pairs, original_atts, treatment_groups)

bootstrap_group = {}

for g, info in group_agg_info.items():

idx = info["gt_indices"]

w = info["weights"]

with np.errstate(divide="ignore", invalid="ignore", over="ignore"):

bootstrap_group[g] = bootstrap_atts[:, idx] @ w

# Compute statistics

gt_ses: Dict[Tuple[Any, Any], float] = {}

gt_cis: Dict[Tuple[Any, Any], Tuple[float, float]] = {}

gt_pvals: Dict[Tuple[Any, Any], float] = {}

for j, gt in enumerate(gt_pairs):

se, ci, pv = _compute_effect_bootstrap_stats_func(

original_atts[j],

bootstrap_atts[:, j],

alpha=self.alpha,

context=f"ATT(g={gt[0]}, t={gt[1]})",

)

gt_ses[gt] = se

gt_cis[gt] = ci

gt_pvals[gt] = pv

if skip_overall:

ov_se, ov_ci, ov_pv = np.nan, (np.nan, np.nan), np.nan

else:

ov_se, ov_ci, ov_pv = _compute_effect_bootstrap_stats_func(

original_overall,

bootstrap_overall,

alpha=self.alpha,

context="overall ATT",

)

es_ses = es_cis = es_pvs = None

if bootstrap_event_study is not None and event_study_info is not None:

es_ses, es_cis, es_pvs = {}, {}, {}

for e in sorted(event_study_info.keys()):

se, ci, pv = _compute_effect_bootstrap_stats_func(

event_study_info[e]["effect"],

bootstrap_event_study[e],

alpha=self.alpha,

context=f"event study (e={e})",

)

es_ses[e] = se

es_cis[e] = ci

es_pvs[e] = pv

g_ses = g_cis = g_pvs = None

if bootstrap_group is not None and group_agg_info is not None:

g_ses, g_cis, g_pvs = {}, {}, {}

for g in sorted(group_agg_info.keys()):

se, ci, pv = _compute_effect_bootstrap_stats_func(

group_agg_info[g]["effect"],

bootstrap_group[g],

alpha=self.alpha,

context=f"group effect (g={g})",

)

g_ses[g] = se

g_cis[g] = ci

g_pvs[g] = pv

return EDiDBootstrapResults(

n_bootstrap=self.n_bootstrap,

weight_type=self.bootstrap_weights,

alpha=self.alpha,

overall_att_se=ov_se,

overall_att_ci=ov_ci,

overall_att_p_value=ov_pv,

group_time_ses=gt_ses,

group_time_cis=gt_cis,

group_time_p_values=gt_pvals,

event_study_ses=es_ses,

event_study_cis=es_cis,

event_study_p_values=es_pvs,

group_effect_ses=g_ses,

group_effect_cis=g_cis,

group_effect_p_values=g_pvs,

bootstrap_distribution=bootstrap_overall,

)

def _prepare_es_agg_boot(

self,

gt_pairs: List[Tuple[Any, Any]],

original_atts: np.ndarray,

cohort_fractions: Dict[float, float],

balance_e: Optional[int],

) -> Dict[int, Dict[str, Any]]:

"""Prepare event-study aggregation info for bootstrap."""

effects_by_e: Dict[int, List[Tuple[int, float, float]]] = {}

for j, (g, t) in enumerate(gt_pairs):

if not np.isfinite(original_atts[j]):

continue # Skip NaN cells

e = t - g

if e not in effects_by_e:

effects_by_e[e] = []

effects_by_e[e].append((j, original_atts[j], cohort_fractions.get(g, 0.0)))

if balance_e is not None:

groups_at_e = {

gt_pairs[j][0]

for j, (g, t) in enumerate(gt_pairs)

if t - g == balance_e and np.isfinite(original_atts[j])

}

balanced: Dict[int, List[Tuple[int, float, float]]] = {}

for j, (g, t) in enumerate(gt_pairs):

if g in groups_at_e:

if not np.isfinite(original_atts[j]):

continue # Skip NaN cells even in balanced set

e = t - g

if e not in balanced:

balanced[e] = []

balanced[e].append((j, original_atts[j], cohort_fractions.get(g, 0.0)))

effects_by_e = balanced

if balance_e is not None and not effects_by_e:

warnings.warn(

f"balance_e={balance_e}: no cohort has a finite effect at the "

"anchor horizon. Event study will be empty.",

UserWarning,

stacklevel=2,

)

result = {}

for e, elist in effects_by_e.items():

indices = np.array([x[0] for x in elist])

effs = np.array([x[1] for x in elist])

pgs = np.array([x[2] for x in elist])

w = pgs / pgs.sum() if pgs.sum() > 0 else np.ones(len(pgs)) / len(pgs)

result[e] = {

"gt_indices": indices,

"weights": w,

"effect": float(np.sum(w * effs)),

}

return result

def _prepare_group_agg_boot(

self,

gt_pairs: List[Tuple[Any, Any]],

original_atts: np.ndarray,

treatment_groups: List[Any],

) -> Dict[Any, Dict[str, Any]]:

"""Prepare group-level aggregation info for bootstrap."""

result = {}

for g in treatment_groups:

group_data = [

(j, original_atts[j])

for j, (gg, t) in enumerate(gt_pairs)

if gg == g and t >= g - self.anticipation and np.isfinite(original_atts[j])

]

if not group_data:

continue

indices = np.array([x[0] for x in group_data])

effs = np.array([x[1] for x in group_data])

w = np.ones(len(effs)) / len(effs)

result[g] = {

"gt_indices": indices,

"weights": w,

"effect": float(np.sum(w * effs)),

}

return result

diff-diff/diff_diff/efficient_did_bootstrap.py at main · UtilityTools/diff-diff · GitHub

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