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

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"""

Staggered Difference-in-Differences estimators.

Implements modern methods for DiD with variation in treatment timing,

including the Callaway-Sant'Anna (2021) estimator.

"""

import warnings

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

import numpy as np

import pandas as pd

from scipy import linalg as scipy_linalg

from diff_diff.linalg import (

_check_propensity_diagnostics,

_detect_rank_deficiency,

_format_dropped_columns,

solve_logit,

solve_ols,

)

from diff_diff.staggered_aggregation import (

CallawaySantAnnaAggregationMixin,

)

from diff_diff.staggered_bootstrap import (

CallawaySantAnnaBootstrapMixin,

CSBootstrapResults,

)

# Import from split modules

from diff_diff.staggered_results import (

CallawaySantAnnaResults,

GroupTimeEffect,

)

from diff_diff.utils import safe_inference, safe_inference_batch

# Re-export for backward compatibility

__all__ = [

"CallawaySantAnna",

"CallawaySantAnnaResults",

"CSBootstrapResults",

"GroupTimeEffect",

]

# Type alias for pre-computed structures

PrecomputedData = Dict[str, Any]

def _linear_regression(

X: np.ndarray,

y: np.ndarray,

rank_deficient_action: str = "warn",

weights: Optional[np.ndarray] = None,

) -> Tuple[np.ndarray, np.ndarray]:

"""

Fit OLS regression.

Parameters

----------

X : np.ndarray

Feature matrix (n_samples, n_features). Intercept added automatically.

y : np.ndarray

Outcome variable.

rank_deficient_action : str, default "warn"

Action when design matrix is rank-deficient:

- "warn": Issue warning and drop linearly dependent columns (default)

- "error": Raise ValueError

- "silent": Drop columns silently without warning

weights : np.ndarray, optional

Observation weights for WLS. When None, OLS is used.

Returns

-------

beta : np.ndarray

Fitted coefficients (including intercept).

residuals : np.ndarray

Residuals from the fit.

"""

n = X.shape[0]

# Add intercept

X_with_intercept = np.column_stack([np.ones(n), X])

# Use unified OLS backend (no vcov needed)

beta, residuals, _ = solve_ols(

X_with_intercept,

return_vcov=False,

rank_deficient_action=rank_deficient_action,

weights=weights,

)

return beta, residuals

def _safe_inv(

A: np.ndarray,

tracker: Optional[list] = None,

) -> np.ndarray:

"""Invert a square matrix with lstsq fallback for near-singular cases.

Parameters

----------

A : np.ndarray

Square matrix to invert.

tracker : list, optional

When provided, one condition-number sample of ``A`` is appended on

every LinAlgError fallback. ``CallawaySantAnna.fit()`` initializes

a list and emits a single aggregate `UserWarning` after the fit

finishes, rather than surfacing a separate warning per fallback.

Sibling of finding #17 in the Phase 2 silent-failures audit.

"""

try:

return np.linalg.solve(A, np.eye(A.shape[0]))

except np.linalg.LinAlgError:

if tracker is not None:

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

tracker.append(float(np.linalg.cond(A)))

return np.linalg.lstsq(A, np.eye(A.shape[0]), rcond=None)[0]

class CallawaySantAnna(

CallawaySantAnnaBootstrapMixin,

CallawaySantAnnaAggregationMixin,

"""

Callaway-Sant'Anna (2021) estimator for staggered Difference-in-Differences.

This estimator handles DiD designs with variation in treatment timing

(staggered adoption) and heterogeneous treatment effects. It avoids the

bias of traditional two-way fixed effects (TWFE) estimators by:

1. Computing group-time average treatment effects ATT(g,t) for each

cohort g (units first treated in period g) and time t.

2. Aggregating these to summary measures (overall ATT, event study, etc.)

using appropriate weights.

Parameters

----------

control_group : str, default="never_treated"

Which units to use as controls:

- "never_treated": Use only never-treated units (recommended)

- "not_yet_treated": Use never-treated and not-yet-treated units

anticipation : int, default=0

Number of periods before treatment where effects may occur.

Set to > 0 if treatment effects can begin before the official

treatment date.

estimation_method : str, default="dr"

Estimation method:

- "dr": Doubly robust (recommended)

- "ipw": Inverse probability weighting

- "reg": Outcome regression

alpha : float, default=0.05

Significance level for confidence intervals.

cluster : str, optional

Column name for cluster-robust standard errors.

Defaults to unit-level clustering.

n_bootstrap : int, default=0

Number of bootstrap iterations for inference.

If 0, uses analytical standard errors.

Recommended: 999 or more for reliable inference.

.. note:: Memory Usage

The bootstrap stores all weights in memory as a (n_bootstrap, n_units)

float64 array. For large datasets, this can be significant:

- 1K bootstrap × 10K units = ~80 MB

- 10K bootstrap × 100K units = ~8 GB

Consider reducing n_bootstrap if memory is constrained.

bootstrap_weights : str, default="rademacher"

Type of weights for multiplier bootstrap:

- "rademacher": +1/-1 with equal probability (standard choice)

- "mammen": Two-point distribution (asymptotically valid, matches skewness)

- "webb": Six-point distribution (recommended when n_clusters < 20)

seed : int, optional

Random seed for reproducibility.

rank_deficient_action : str, default="warn"

Action when design matrix is rank-deficient (linearly dependent columns):

- "warn": Issue warning and drop linearly dependent columns (default)

- "error": Raise ValueError

- "silent": Drop columns silently without warning

base_period : str, default="varying"

Method for selecting the base (reference) period for computing

ATT(g,t). Options:

- "varying": For pre-treatment periods (t < g - anticipation), use

t-1 as base (consecutive comparisons). For post-treatment, use

g-1-anticipation. Requires t-1 to exist in data.

- "universal": Always use g-1-anticipation as base period.

Both produce identical post-treatment effects. Matches R's

did::att_gt() base_period parameter.

cband : bool, default=True

Whether to compute simultaneous confidence bands (sup-t) for

event study aggregation. Requires ``n_bootstrap > 0``.

When True, results include ``cband_crit_value`` and per-event-time

``cband_conf_int`` entries controlling family-wise error rate.

pscore_trim : float, default=0.01

Trimming bound for propensity scores. Scores are clipped to

``[pscore_trim, 1 - pscore_trim]`` before weight computation

in IPW and DR estimation. Must be in ``(0, 0.5)``.

panel : bool, default=True

Whether the data is a balanced/unbalanced panel (units observed

across multiple time periods). Set to ``False`` for stationary

repeated cross-sections where each observation has a unique unit

ID and units do not repeat across periods. Requires that the

cross-sectional samples are drawn from the same population in

each period (stationarity). Uses cross-sectional DRDID

(Sant'Anna & Zhao 2020, Section 4) with per-observation influence

functions.

epv_threshold : float, default=10

Events Per Variable threshold for propensity score logit.

When the ratio of minority-class observations to predictor

variables (excluding intercept) falls below this value, a

warning is emitted (or ``ValueError`` raised if

``rank_deficient_action="error"``). Based on Peduzzi et al.

(1996). Only applies to IPW and DR estimation methods.

Use ``diagnose_propensity()`` for a pre-estimation check across

all cohorts.

pscore_fallback : str, default="error"

Action when propensity score estimation fails entirely

(``LinAlgError`` or ``ValueError`` from IRLS):

- "error": Raise the exception (default). Ensures the user is

aware of estimation failures.

- "unconditional": Fall back to unconditional propensity

with a warning. For IPW, this drops all covariates. For DR,

the propensity model becomes unconditional but outcome

regression still uses covariates.

When ``rank_deficient_action="error"``, errors are always

re-raised regardless of this setting.

Attributes

----------

results_ : CallawaySantAnnaResults

Estimation results after calling fit().

is_fitted_ : bool

Whether the model has been fitted.

Examples

--------

Basic usage:

>>> import pandas as pd

>>> from diff_diff import CallawaySantAnna

>>>

>>> # Panel data with staggered treatment

>>> # 'first_treat' = period when unit was first treated (0 if never treated)

>>> data = pd.DataFrame({

... 'unit': [...],

... 'time': [...],

... 'outcome': [...],

... 'first_treat': [...] # 0 for never-treated, else first treatment period

... })

>>>

>>> cs = CallawaySantAnna()

>>> results = cs.fit(data, outcome='outcome', unit='unit',

... time='time', first_treat='first_treat')

>>>

>>> results.print_summary()

With event study aggregation:

>>> cs = CallawaySantAnna()

>>> results = cs.fit(data, outcome='outcome', unit='unit',

... time='time', first_treat='first_treat',

... aggregate='event_study')

>>>

>>> # Plot event study

>>> from diff_diff import plot_event_study

>>> plot_event_study(results)

With covariate adjustment (conditional parallel trends):

>>> # When parallel trends only holds conditional on covariates

>>> cs = CallawaySantAnna(estimation_method='dr') # doubly robust

>>> results = cs.fit(data, outcome='outcome', unit='unit',

... time='time', first_treat='first_treat',

... covariates=['age', 'income'])

>>>

>>> # DR is recommended: consistent if either outcome model

>>> # or propensity model is correctly specified

Notes

-----

The key innovation of Callaway & Sant'Anna (2021) is the disaggregated

approach: instead of estimating a single treatment effect, they estimate

ATT(g,t) for each cohort-time pair. This avoids the "forbidden comparison"

problem where already-treated units act as controls.

The ATT(g,t) is identified under parallel trends conditional on covariates:

E[Y(0)_t - Y(0)_g-1 | G=g] = E[Y(0)_t - Y(0)_g-1 | C=1]

where G=g indicates treatment cohort g and C=1 indicates control units.

This uses g-1 as the base period, which applies to post-treatment (t >= g).

With base_period="varying" (default), pre-treatment uses t-1 as base for

consecutive comparisons useful in parallel trends diagnostics.

References

----------

Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-Differences with

multiple time periods. Journal of Econometrics, 225(2), 200-230.

"""

def __init__(

self,

control_group: str = "never_treated",

anticipation: int = 0,

estimation_method: str = "dr",

alpha: float = 0.05,

cluster: Optional[str] = None,

n_bootstrap: int = 0,

bootstrap_weights: Optional[str] = None,

seed: Optional[int] = None,

rank_deficient_action: str = "warn",

base_period: str = "varying",

cband: bool = True,

pscore_trim: float = 0.01,

panel: bool = True,

epv_threshold: float = 10,

pscore_fallback: str = "error",

import warnings

if control_group not in ["never_treated", "not_yet_treated"]:

raise ValueError(

f"control_group must be 'never_treated' or 'not_yet_treated', "

f"got '{control_group}'"

)

if estimation_method not in ["dr", "ipw", "reg"]:

raise ValueError(

f"estimation_method must be 'dr', 'ipw', or 'reg', " f"got '{estimation_method}'"

)

if not (0 < pscore_trim < 0.5):

raise ValueError(f"pscore_trim must be in (0, 0.5), got {pscore_trim}")

if epv_threshold <= 0:

raise ValueError(f"epv_threshold must be > 0, got {epv_threshold}")

if pscore_fallback not in ["error", "unconditional"]:

raise ValueError(

f"pscore_fallback must be 'error' or 'unconditional', " f"got '{pscore_fallback}'"

)

# Default to rademacher if not specified

if bootstrap_weights is None:

bootstrap_weights = "rademacher"

if bootstrap_weights not in ["rademacher", "mammen", "webb"]:

raise ValueError(

f"bootstrap_weights must be 'rademacher', 'mammen', or 'webb', "

f"got '{bootstrap_weights}'"

)

if rank_deficient_action not in ["warn", "error", "silent"]:

raise ValueError(

f"rank_deficient_action must be 'warn', 'error', or 'silent', "

f"got '{rank_deficient_action}'"

)

if base_period not in ["varying", "universal"]:

raise ValueError(

f"base_period must be 'varying' or 'universal', " f"got '{base_period}'"

)

self.control_group = control_group

self.anticipation = anticipation

self.estimation_method = estimation_method

self.alpha = alpha

self.cluster = cluster

self.n_bootstrap = n_bootstrap

self.bootstrap_weights = bootstrap_weights

self.seed = seed

self.rank_deficient_action = rank_deficient_action

self.base_period = base_period

self.cband = cband

self.pscore_trim = pscore_trim

self.panel = panel

self.epv_threshold = epv_threshold

self.pscore_fallback = pscore_fallback

self.is_fitted_ = False

self.results_: Optional[CallawaySantAnnaResults] = None

def diagnose_propensity(

self,

df: pd.DataFrame,

outcome: str,

unit: str,

time: str,

first_treat: str,

covariates: Optional[List[str]] = None,

) -> pd.DataFrame:

"""

Check Events Per Variable (EPV) across all cohorts without estimation.

Examines the data to identify cohorts where propensity score logit may

be unreliable due to too few events per covariate. Based on Peduzzi

et al. (1996).

This is a raw-count heuristic: it uses total cohort/control unit

counts without filtering for missing outcomes, zero survey weights,

or period-specific validity. The actual fit-time EPV (stored in

``results.epv_diagnostics``) may be lower because ``fit()`` operates

on the valid base/post outcome pair and the positive-weight effective

sample. Use this method as a quick pre-check; rely on

``results.epv_diagnostics`` for authoritative per-cell EPV.

Parameters

----------

df, outcome, unit, time, first_treat, covariates

Same arguments as ``fit()``.

Returns

-------

pd.DataFrame

Per-cohort EPV diagnostics with columns: group, n_treated,

n_control, n_covariates, n_params, epv, status.

"""

if not self.panel:

raise NotImplementedError(

"diagnose_propensity() is not yet supported for repeated "

"cross-section data (panel=False). Use fit() with covariates "

"and check results.epv_diagnostics instead."

)

if self.control_group == "not_yet_treated":

raise NotImplementedError(

"diagnose_propensity() is not yet supported for "

"control_group='not_yet_treated' because the control set "

"varies per (g, t) cell. Use fit() with covariates and "

"check results.epv_diagnostics instead."

)

if self.estimation_method == "reg":

return pd.DataFrame(

columns=[

"group",

"n_treated",

"n_control",

"n_covariates",

"n_params",

"epv",

"status",

]

)

if not covariates:

return pd.DataFrame(

columns=[

"group",

"n_treated",

"n_control",

"n_covariates",

"n_params",

"epv",

"status",

]

)

# Normalize np.inf → 0 for never-treated encoding (same as fit())

df = df.copy()

_inf_mask_diag = df[first_treat].isin([np.inf, float("inf")])

if _inf_mask_diag.any():

n_inf_units = df.loc[_inf_mask_diag, unit].nunique()

warnings.warn(

f"{n_inf_units} unit(s) have first_treat=inf; recoding to 0 "

f"(never-treated). Use first_treat=0 to suppress this warning.",

UserWarning,

stacklevel=2,

)

df[first_treat] = df[first_treat].replace([np.inf, float("inf")], 0)

# Compute time_periods and treatment_groups (same logic as fit())

time_periods = sorted(df[time].unique())

treatment_groups = sorted([g for g in df[first_treat].unique() if g > 0])

precomputed = self._precompute_structures(

df,

outcome,

unit,

time,

first_treat,

covariates,

time_periods=time_periods,

treatment_groups=treatment_groups,

)

cohort_masks = precomputed["cohort_masks"]

never_treated_mask = precomputed["never_treated_mask"]

unit_cohorts = precomputed["unit_cohorts"]

n_covariates = len(covariates)

n_params = n_covariates # predictor count, excluding intercept (Peduzzi convention)

rows = []

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

treated_mask = cohort_masks[g]

if self.control_group == "never_treated":

control_mask = never_treated_mask

else:

base_period_val = g - 1 - self.anticipation

nyt_threshold = base_period_val + self.anticipation

control_mask = never_treated_mask | (

(unit_cohorts > nyt_threshold) & (unit_cohorts != g)

)

n_treated = int(np.sum(treated_mask))

n_control = int(np.sum(control_mask))

n_events = min(n_treated, n_control)

epv = n_events / n_params if n_params > 0 else float("inf")

if epv >= self.epv_threshold:

status = "ok"

elif epv >= 2:

status = "low"

else:

status = "critical"

rows.append(

{

"group": g,

"n_treated": n_treated,

"n_control": n_control,

"n_covariates": n_covariates,

"n_params": n_params,

"epv": round(epv, 1),

"status": status,

}

)

return pd.DataFrame(rows)

@staticmethod

def _collapse_survey_to_unit_level(resolved_survey, df, unit_col, all_units):

"""Create unit-level ResolvedSurveyDesign for panel IF-based variance.

Survey design columns are constant within units (validated upstream).

This extracts one row per unit, aligned to ``all_units`` ordering.

"""

from diff_diff.survey import collapse_survey_to_unit_level

return collapse_survey_to_unit_level(resolved_survey, df, unit_col, all_units)

def _precompute_structures(

self,

df: pd.DataFrame,

outcome: str,

unit: str,

time: str,

first_treat: str,

covariates: Optional[List[str]],

time_periods: List[Any],

treatment_groups: List[Any],

resolved_survey=None,

) -> PrecomputedData:

"""

Pre-compute data structures for efficient ATT(g,t) computation.

This pivots data to wide format and pre-computes:

- Outcome matrix (units x time periods)

- Covariate matrix (units x covariates) from base period

- Unit cohort membership masks

- Control unit masks

Returns

-------

PrecomputedData

Dictionary with pre-computed structures.

"""

# Get unique units and their cohort assignments

unit_info = df.groupby(unit)[first_treat].first()

all_units = unit_info.index.values

unit_cohorts = unit_info.values

# Create unit index mapping for fast lookups

unit_to_idx = {u: i for i, u in enumerate(all_units)}

# Pivot outcome to wide format: rows = units, columns = time periods

outcome_wide = df.pivot(index=unit, columns=time, values=outcome)

# Reindex to ensure all units are present (handles unbalanced panels)

outcome_wide = outcome_wide.reindex(all_units)

outcome_matrix = outcome_wide.values # Shape: (n_units, n_periods)

period_to_col = {t: i for i, t in enumerate(outcome_wide.columns)}

# Pre-compute cohort masks (boolean arrays)

cohort_masks = {}

for g in treatment_groups:

cohort_masks[g] = unit_cohorts == g

# Never-treated mask

# np.inf was normalized to 0 in fit(), so the np.inf check is defensive only

never_treated_mask = (unit_cohorts == 0) | (unit_cohorts == np.inf)

# Pre-compute covariate matrices by time period if needed

# (covariates are retrieved from the base period of each comparison)

covariate_by_period = None

if covariates:

covariate_by_period = {}

for t in time_periods:

period_data = df[df[time] == t].set_index(unit)

period_cov = period_data.reindex(all_units)[covariates]

covariate_by_period[t] = period_cov.values # Shape: (n_units, n_covariates)

is_balanced = not np.any(np.isnan(outcome_matrix))

# Extract per-unit survey weights (one weight per unit)

if resolved_survey is not None:

sw_by_unit = (

pd.Series(resolved_survey.weights, index=df.index).groupby(df[unit]).first()

)

survey_weights_arr = sw_by_unit.reindex(all_units).values

else:

survey_weights_arr = None

resolved_survey_unit = (

self._collapse_survey_to_unit_level(resolved_survey, df, unit, all_units)

if resolved_survey is not None

else None

)

return {

"all_units": all_units,

"unit_to_idx": unit_to_idx,

"unit_cohorts": unit_cohorts,

"outcome_matrix": outcome_matrix,

"period_to_col": period_to_col,

"cohort_masks": cohort_masks,

"never_treated_mask": never_treated_mask,

"covariate_by_period": covariate_by_period,

"time_periods": time_periods,

"is_balanced": is_balanced,

"is_panel": True,

"canonical_size": len(all_units),

"survey_weights": survey_weights_arr,

"resolved_survey": resolved_survey,

"resolved_survey_unit": resolved_survey_unit,

"df_survey": (

resolved_survey_unit.df_survey if resolved_survey_unit is not None else None

}

def _compute_att_gt_fast(

self,

precomputed: PrecomputedData,

g: Any,

t: Any,

covariates: Optional[List[str]],

pscore_cache: Optional[Dict] = None,

cho_cache: Optional[Dict] = None,

epv_diagnostics: Optional[Dict] = None,

) -> Tuple[Optional[float], float, int, int, Optional[Dict[str, Any]], Optional[float]]:

"""

Compute ATT(g,t) using pre-computed data structures (fast version).

Uses vectorized numpy operations on pre-pivoted outcome matrix

instead of repeated pandas filtering.

Returns

-------

att_gt : float or None

se_gt : float

n_treated : int

n_control : int

inf_func_info : dict or None

survey_weight_sum : float or None

Sum of survey weights for treated units (for aggregation weighting).

"""

period_to_col = precomputed["period_to_col"]

outcome_matrix = precomputed["outcome_matrix"]

cohort_masks = precomputed["cohort_masks"]

never_treated_mask = precomputed["never_treated_mask"]

unit_cohorts = precomputed["unit_cohorts"]

covariate_by_period = precomputed["covariate_by_period"]

# Base period selection based on mode

if self.base_period == "universal":

# Universal: always use g - 1 - anticipation

base_period_val = g - 1 - self.anticipation

else: # varying

if t < g - self.anticipation:

# Pre-treatment: use t - 1 (consecutive comparison)

base_period_val = t - 1

else:

# Post-treatment: use g - 1 - anticipation

base_period_val = g - 1 - self.anticipation

if base_period_val not in period_to_col:

# Base period must exist; no fallback to maintain methodological consistency

return None, 0.0, 0, 0, None, None

# Check if periods exist in the data

if base_period_val not in period_to_col or t not in period_to_col:

return None, 0.0, 0, 0, None, None

base_col = period_to_col[base_period_val]

post_col = period_to_col[t]

# Get treated units mask (cohort g)

treated_mask = cohort_masks[g]

# Get control units mask

if self.control_group == "never_treated":

control_mask = never_treated_mask

else: # not_yet_treated

# Not yet treated at BOTH time t and the base period:

# Controls must be untreated at whichever is later, otherwise

# their outcome at the base period is contaminated by treatment.

nyt_threshold = max(t, base_period_val) + self.anticipation

control_mask = never_treated_mask | (

(unit_cohorts > nyt_threshold) & (unit_cohorts != g)

)

# Extract outcomes for base and post periods

y_base = outcome_matrix[:, base_col]

y_post = outcome_matrix[:, post_col]

# Compute outcome changes (vectorized)

outcome_change = y_post - y_base

# Filter to units with valid data (no NaN in either period)

valid_mask = ~(np.isnan(y_base) | np.isnan(y_post))

# Get treated and control with valid data

treated_valid = treated_mask & valid_mask

control_valid = control_mask & valid_mask

n_treated = np.sum(treated_valid)

n_control = np.sum(control_valid)

if n_treated == 0 or n_control == 0:

return None, 0.0, 0, 0, None, None

# Extract outcome changes for treated and control

treated_change = outcome_change[treated_valid]

control_change = outcome_change[control_valid]

# Extract survey weights for treated and control

survey_w = precomputed.get("survey_weights")

sw_treated = survey_w[treated_valid] if survey_w is not None else None

sw_control = survey_w[control_valid] if survey_w is not None else None

# Guard against zero effective mass after subpopulation filtering

if sw_treated is not None and np.sum(sw_treated) <= 0:

return None, 0.0, 0, 0, None, None

if sw_control is not None and np.sum(sw_control) <= 0:

return None, 0.0, 0, 0, None, None

# Get covariates if specified (from the base period)

X_treated = None

X_control = None

if covariates and covariate_by_period is not None:

cov_matrix = covariate_by_period[base_period_val]

X_treated = cov_matrix[treated_valid]

X_control = cov_matrix[control_valid]

# Check for missing values

if np.any(np.isnan(X_treated)) or np.any(np.isnan(X_control)):

warnings.warn(

f"Missing values in covariates for group {g}, time {t}. "

"Falling back to unconditional estimation.",

UserWarning,

stacklevel=3,

)

X_treated = None

X_control = None

# Compute cache key for propensity score reuse

pscore_key = None

if pscore_cache is not None and X_treated is not None:

is_balanced = precomputed.get("is_balanced", False)

if is_balanced and self.control_group == "never_treated":

pscore_key = (g, base_period_val)

else:

pscore_key = (g, base_period_val, t)

# Compute cache key for Cholesky reuse (DR outcome regression)

cho_key = None

if cho_cache is not None and X_control is not None:

is_balanced = precomputed.get("is_balanced", False)

if is_balanced and self.control_group == "never_treated":

cho_key = base_period_val

else:

cho_key = (g, base_period_val, t)

# Estimation method

if self.estimation_method == "reg":

att_gt, se_gt, inf_func = self._outcome_regression(

treated_change,

control_change,

X_treated,

X_control,

sw_treated=sw_treated,

sw_control=sw_control,

)

elif self.estimation_method == "ipw":

sw_all = np.concatenate([sw_treated, sw_control]) if sw_treated is not None else None

epv_diag: dict = {}

att_gt, se_gt, inf_func = self._ipw_estimation(

treated_change,

control_change,

int(n_treated),

int(n_control),

X_treated,

X_control,

pscore_cache=pscore_cache,

pscore_key=pscore_key,

sw_treated=sw_treated,

sw_control=sw_control,

sw_all=sw_all,

context_label=f"cohort g={g}",

epv_diagnostics_out=epv_diag,

)

if epv_diagnostics is not None and epv_diag:

epv_diagnostics[(g, t)] = epv_diag

else: # doubly robust

sw_all = np.concatenate([sw_treated, sw_control]) if sw_treated is not None else None

epv_diag = {}

att_gt, se_gt, inf_func = self._doubly_robust(

treated_change,

control_change,

X_treated,

X_control,

pscore_cache=pscore_cache,

pscore_key=pscore_key,

cho_cache=cho_cache,

cho_key=cho_key,

sw_treated=sw_treated,

sw_control=sw_control,

sw_all=sw_all,

context_label=f"cohort g={g}",

epv_diagnostics_out=epv_diag,

)

if epv_diagnostics is not None and epv_diag:

epv_diagnostics[(g, t)] = epv_diag

# Package influence function info with index arrays (positions into

# precomputed['all_units']) for O(1) downstream lookups instead of

# O(n) Python dict lookups.

n_t = int(n_treated)

all_units = precomputed["all_units"]

treated_positions = np.where(treated_valid)[0]

control_positions = np.where(control_valid)[0]

inf_func_info = {

"treated_idx": treated_positions,

"control_idx": control_positions,

"treated_units": all_units[treated_positions],

"control_units": all_units[control_positions],

"treated_inf": inf_func[:n_t],

"control_inf": inf_func[n_t:],

}

sw_sum = float(np.sum(sw_treated)) if sw_treated is not None else None

return att_gt, se_gt, int(n_treated), int(n_control), inf_func_info, sw_sum

def _compute_all_att_gt_vectorized(

self,

precomputed: PrecomputedData,

treatment_groups: List[Any],

time_periods: List[Any],

min_period: Any,

) -> Tuple[Dict, Dict, Dict]:

"""

Vectorized computation of all ATT(g,t) for the no-covariates regression case.

This inlines the simple difference-in-means path from _outcome_regression()

and eliminates per-(g,t) Python function call overhead.

Returns

-------

group_time_effects : dict

Mapping (g, t) -> effect dict.

influence_func_info : dict

Mapping (g, t) -> influence function info dict.

"""

period_to_col = precomputed["period_to_col"]

outcome_matrix = precomputed["outcome_matrix"]

cohort_masks = precomputed["cohort_masks"]

never_treated_mask = precomputed["never_treated_mask"]

unit_cohorts = precomputed["unit_cohorts"]

survey_w = precomputed.get("survey_weights")

group_time_effects = {}

influence_func_info = {}

skipped_missing_period: List[Tuple] = []

skipped_empty_cell: List[Tuple] = []

# Collect all valid (g, t, base_col, post_col) tuples

tasks = []

for g in treatment_groups:

if self.base_period == "universal":

universal_base = g - 1 - self.anticipation

valid_periods = [t for t in time_periods if t != universal_base]

else:

valid_periods = [

t for t in time_periods if t >= g - self.anticipation or t > min_period

]

for t in valid_periods:

# Base period selection

if self.base_period == "universal":

base_period_val = g - 1 - self.anticipation

else:

if t < g - self.anticipation:

base_period_val = t - 1

else:

base_period_val = g - 1 - self.anticipation

if base_period_val not in period_to_col or t not in period_to_col:

skipped_missing_period.append((g, t))

continue

tasks.append(

(g, t, period_to_col[base_period_val], period_to_col[t], base_period_val)

)

# Process all tasks

atts = []

ses = []

task_keys = []

for g, t, base_col, post_col, base_period_val in tasks:

treated_mask = cohort_masks[g]

if self.control_group == "never_treated":

control_mask = never_treated_mask

else:

# Controls must be untreated at both t and base_period_val

nyt_threshold = max(t, base_period_val) + self.anticipation

control_mask = never_treated_mask | (

(unit_cohorts > nyt_threshold) & (unit_cohorts != g)

)

y_base = outcome_matrix[:, base_col]

y_post = outcome_matrix[:, post_col]

outcome_change = y_post - y_base

valid_mask = ~(np.isnan(y_base) | np.isnan(y_post))

treated_valid = treated_mask & valid_mask

control_valid = control_mask & valid_mask

n_treated = np.sum(treated_valid)

n_control = np.sum(control_valid)

if n_treated == 0 or n_control == 0:

skipped_empty_cell.append((g, t))

continue

treated_change = outcome_change[treated_valid]

control_change = outcome_change[control_valid]

n_t = int(n_treated)

n_c = int(n_control)

# Inline no-covariates regression (difference in means)

if survey_w is not None:

sw_t = survey_w[treated_valid]

sw_c = survey_w[control_valid]

# Guard against zero effective mass

if np.sum(sw_t) <= 0 or np.sum(sw_c) <= 0:

skipped_empty_cell.append((g, t))

continue

sw_t_norm = sw_t / np.sum(sw_t)

sw_c_norm = sw_c / np.sum(sw_c)

mu_t = float(np.sum(sw_t_norm * treated_change))

mu_c = float(np.sum(sw_c_norm * control_change))

att = mu_t - mu_c

# Influence function (survey-weighted)

inf_treated = sw_t_norm * (treated_change - mu_t)

inf_control = -sw_c_norm * (control_change - mu_c)

# SE derived from IF: sum(IF_i^2)

se = (

float(np.sqrt(np.sum(inf_treated**2) + np.sum(inf_control**2)))

if (n_t > 0 and n_c > 0)

else 0.0

)

sw_sum = float(np.sum(sw_t))

else:

att = float(np.mean(treated_change) - np.mean(control_change))

var_t = float(np.var(treated_change, ddof=1)) if n_t > 1 else 0.0

var_c = float(np.var(control_change, ddof=1)) if n_c > 1 else 0.0

se = float(np.sqrt(var_t / n_t + var_c / n_c)) if (n_t > 0 and n_c > 0) else 0.0

# Influence function

inf_treated = (treated_change - np.mean(treated_change)) / n_t

inf_control = -(control_change - np.mean(control_change)) / n_c

sw_sum = None

gte_entry = {

"effect": att,

"se": se,

# t_stat, p_value, conf_int filled by batch inference below

"t_stat": np.nan,

"p_value": np.nan,

"conf_int": (np.nan, np.nan),

"n_treated": n_t,

"n_control": n_c,

}

if sw_sum is not None:

gte_entry["survey_weight_sum"] = sw_sum

group_time_effects[(g, t)] = gte_entry

all_units = precomputed["all_units"]

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