"""
Results classes for difference-in-differences estimation.
Provides statsmodels-style output with a more Pythonic interface.
"""
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
def _format_survey_block(sm, width: int) -> list:
"""Format survey design metadata block for summary() output.
Parameters
----------
sm : SurveyMetadata
Survey metadata from results object.
width : int
Total width for separator lines and centering.
"""
label_width = 30 if width >= 80 else 25
lines = [
"",
"-" * width,
"Survey Design".center(width),
"-" * width,
f"{'Weight type:':<{label_width}} {sm.weight_type:>10}",
]
if getattr(sm, "replicate_method", None) is not None:
lines.append(f"{'Replicate method:':<{label_width}} {sm.replicate_method:>10}")
if getattr(sm, "n_replicates", None) is not None:
lines.append(f"{'Replicates:':<{label_width}} {sm.n_replicates:>10}")
else:
if sm.n_strata is not None:
lines.append(f"{'Strata:':<{label_width}} {sm.n_strata:>10}")
if sm.n_psu is not None:
lines.append(f"{'PSU/Cluster:':<{label_width}} {sm.n_psu:>10}")
lines.append(f"{'Effective sample size:':<{label_width}} {sm.effective_n:>10.1f}")
lines.append(f"{'Kish DEFF (weights):':<{label_width}} {sm.design_effect:>10.2f}")
if sm.df_survey is not None:
lines.append(f"{'Survey d.f.:':<{label_width}} {sm.df_survey:>10}")
lines.append("-" * width)
return lines
def _format_vcov_label(
vcov_type: str,
*,
cluster_name: Optional[str],
n_clusters: Optional[int],
n_obs: Optional[int],
conley_lag_cutoff: Optional[int] = None,
) -> Optional[str]:
"""Compose a human-readable variance-family label for summary output.
Returns None when vcov_type is not recognized so the caller can skip the
line silently (backward-compat).
"""
if vcov_type == "classical":
return "Classical OLS SEs (non-robust)"
if vcov_type == "hc1":
if cluster_name:
suffix = f", G={n_clusters}" if n_clusters else ""
return f"CR1 cluster-robust at {cluster_name}{suffix}"
return "HC1 heteroskedasticity-robust"
if vcov_type == "hc2":
return "HC2 leverage-corrected"
if vcov_type == "hc2_bm":
if cluster_name:
suffix = f", G={n_clusters}" if n_clusters else ""
return f"CR2 Bell-McCaffrey cluster-robust at {cluster_name}{suffix}"
suffix = f", n={n_obs}" if n_obs else ""
return f"HC2 + Bell-McCaffrey DOF (one-way{suffix})"
if vcov_type == "conley":
# Cross-sectional Conley on direct LinearRegression / compute_robust_vcov,
# or panel block-decomposed Conley (within-period spatial + within-unit
# Bartlett serial) on DifferenceInDifferences / MultiPeriodDiD /
# TwoWayFixedEffects. With an explicit cluster_name, the combined
# spatial + cluster product kernel applies (Wave A #119).
lag_suffix = f", lag_cutoff={conley_lag_cutoff}" if conley_lag_cutoff is not None else ""
if cluster_name:
return (
f"Conley spatial HAC (1999) + cluster product kernel at {cluster_name}{lag_suffix}"
)
return f"Conley spatial HAC (1999){lag_suffix}"
return None
[docs]
@dataclass
class DiDResults:
"""
Results from a Difference-in-Differences estimation.
Provides easy access to coefficients, standard errors, confidence intervals,
and summary statistics in a Pythonic way.
Attributes
----------
att : float
Average Treatment effect on the Treated (ATT).
se : float
Standard error of the ATT estimate.
t_stat : float
T-statistic for the ATT estimate.
p_value : float
P-value for the null hypothesis that ATT = 0.
conf_int : tuple[float, float]
Confidence interval for the ATT.
n_obs : int
Number of observations used in estimation.
n_treated : int
Number of treated units/observations.
n_control : int
Number of control units/observations.
"""
att: float
se: float
t_stat: float
p_value: float
conf_int: Tuple[float, float]
n_obs: int
n_treated: int
n_control: int
alpha: float = 0.05
coefficients: Optional[Dict[str, float]] = field(default=None)
vcov: Optional[np.ndarray] = field(default=None)
residuals: Optional[np.ndarray] = field(default=None)
fitted_values: Optional[np.ndarray] = field(default=None)
r_squared: Optional[float] = field(default=None)
# Bootstrap inference fields
inference_method: str = field(default="analytical")
n_bootstrap: Optional[int] = field(default=None)
n_clusters: Optional[int] = field(default=None)
bootstrap_distribution: Optional[np.ndarray] = field(default=None, repr=False)
# Survey design metadata (SurveyMetadata instance from diff_diff.survey)
survey_metadata: Optional[Any] = field(default=None)
# Variance-covariance family: "classical" | "hc1" | "hc2" | "hc2_bm" |
# "conley". Plus cluster_name when cluster-robust. Used by summary() to
# label the SE family in the output. For vcov_type='conley' on the panel
# estimators (DifferenceInDifferences / MultiPeriodDiD / TwoWayFixedEffects),
# `conley_lag_cutoff` carries the within-unit Bartlett max lag (matches
# the constructor arg). DiD requires `unit=<col>` as a fit-time kwarg
# when vcov_type='conley' (Wave A #118).
vcov_type: Optional[str] = field(default=None)
cluster_name: Optional[str] = field(default=None)
conley_lag_cutoff: Optional[int] = field(default=None)
[docs]
def __repr__(self) -> str:
"""Concise string representation."""
return (
f"DiDResults(ATT={self.att:.4f}{self.significance_stars}, "
f"SE={self.se:.4f}, "
f"p={self.p_value:.4f})"
)
@property
def coef_var(self) -> float:
"""Coefficient of variation: SE / abs(ATT). NaN when ATT is 0 or SE non-finite."""
if not (np.isfinite(self.se) and self.se >= 0):
return np.nan
if not np.isfinite(self.att) or self.att == 0:
return np.nan
return self.se / abs(self.att)
[docs]
def summary(self, alpha: Optional[float] = None) -> str:
"""
Generate a formatted summary of the estimation results.
Parameters
----------
alpha : float, optional
Significance level for confidence intervals. Defaults to the
alpha used during estimation.
Returns
-------
str
Formatted summary table.
"""
alpha = alpha or self.alpha
conf_level = int((1 - alpha) * 100)
lines = [
"=" * 70,
"Difference-in-Differences Estimation Results".center(70),
"=" * 70,
"",
f"{'Observations:':<25} {self.n_obs:>10}",
f"{'Treated:':<25} {self.n_treated:>10}",
f"{'Control:':<25} {self.n_control:>10}",
]
if self.r_squared is not None:
lines.append(f"{'R-squared:':<25} {self.r_squared:>10.4f}")
# Add survey design info
if self.survey_metadata is not None:
sm = self.survey_metadata
lines.extend(_format_survey_block(sm, 70))
# Add inference method info
if self.inference_method != "analytical":
lines.append(f"{'Inference method:':<25} {self.inference_method:>10}")
if self.n_bootstrap is not None:
lines.append(f"{'Bootstrap replications:':<25} {self.n_bootstrap:>10}")
if self.n_clusters is not None:
lines.append(f"{'Number of clusters:':<25} {self.n_clusters:>10}")
# Add variance family label (vcov_type) only when inference was analytical
# AND no survey design is in play. For wild-bootstrap the reported SE/CI
# come from resampling, so the analytical variance family would mislabel
# the actual inference source. Survey fits use Taylor linearization or
# replicate-weight variance instead of the analytical HC/CR sandwich;
# _format_survey_block above already surfaces the survey inference
# details (weight type, strata/PSU counts, replicate method), so a
# parallel "Variance: HC1/..." line would be misleading. The survey
# suppression also covers MultiPeriodDiDResults.
if (
self.vcov_type is not None
and self.inference_method == "analytical"
and self.survey_metadata is None
):
label = _format_vcov_label(
self.vcov_type,
cluster_name=self.cluster_name,
n_clusters=self.n_clusters,
n_obs=self.n_obs,
conley_lag_cutoff=self.conley_lag_cutoff,
)
if label is not None:
lines.append(f"{'Variance:':<25} {label:>40}")
lines.extend(
[
"",
"-" * 70,
f"{'Parameter':<15} {'Estimate':>12} {'Std. Err.':>12} {'t-stat':>10} {'P>|t|':>10} {'':>5}",
"-" * 70,
f"{'ATT':<15} {self.att:>12.4f} {self.se:>12.4f} {self.t_stat:>10.3f} {self.p_value:>10.4f} {self.significance_stars:>5}",
"-" * 70,
"",
f"{conf_level}% Confidence Interval: [{self.conf_int[0]:.4f}, {self.conf_int[1]:.4f}]",
]
)
cv = self.coef_var
if np.isfinite(cv):
lines.append(f"{'CV (SE/abs(ATT)):':<25} {cv:>10.4f}")
# Add significance codes
lines.extend(
[
"",
"Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1",
"=" * 70,
]
)
return "\n".join(lines)
[docs]
def print_summary(self, alpha: Optional[float] = None) -> None:
"""Print the summary to stdout."""
print(self.summary(alpha))
[docs]
def to_dict(self) -> Dict[str, Any]:
"""
Convert results to a dictionary.
Returns
-------
Dict[str, Any]
Dictionary containing all estimation results.
"""
result = {
"att": self.att,
"se": self.se,
"t_stat": self.t_stat,
"p_value": self.p_value,
"conf_int_lower": self.conf_int[0],
"conf_int_upper": self.conf_int[1],
"n_obs": self.n_obs,
"n_treated": self.n_treated,
"n_control": self.n_control,
"r_squared": self.r_squared,
"inference_method": self.inference_method,
}
# Variance-family metadata: included only when set, so existing
# dict consumers see no new keys for non-conley / non-cluster fits.
if self.vcov_type is not None:
result["vcov_type"] = self.vcov_type
if self.cluster_name is not None:
result["cluster_name"] = self.cluster_name
if self.conley_lag_cutoff is not None:
result["conley_lag_cutoff"] = self.conley_lag_cutoff
if self.n_bootstrap is not None:
result["n_bootstrap"] = self.n_bootstrap
if self.n_clusters is not None:
result["n_clusters"] = self.n_clusters
if self.survey_metadata is not None:
sm = self.survey_metadata
result["weight_type"] = sm.weight_type
result["effective_n"] = sm.effective_n
result["design_effect"] = sm.design_effect
result["sum_weights"] = sm.sum_weights
result["n_strata"] = sm.n_strata
result["n_psu"] = sm.n_psu
result["df_survey"] = sm.df_survey
return result
[docs]
def to_dataframe(self) -> pd.DataFrame:
"""
Convert results to a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with estimation results.
"""
return pd.DataFrame([self.to_dict()])
@property
def is_significant(self) -> bool:
"""Check if the ATT is statistically significant at the alpha level."""
return bool(self.p_value < self.alpha)
@property
def significance_stars(self) -> str:
"""Return significance stars based on p-value."""
return _get_significance_stars(self.p_value)
def _get_significance_stars(p_value: float) -> str:
"""Return significance stars based on p-value.
Returns empty string for NaN p-values (unidentified coefficients from
rank-deficient matrices).
"""
import numpy as np
if np.isnan(p_value):
return ""
if p_value < 0.001:
return "***"
elif p_value < 0.01:
return "**"
elif p_value < 0.05:
return "*"
elif p_value < 0.1:
return "."
return ""
[docs]
@dataclass
class SpilloverDiDResults(DiDResults):
"""Results from a ring-indicator spillover-aware DiD estimation (Butts 2021).
Extends :class:`DiDResults` with per-ring spillover effect estimates and
diagnostic counts for the identifying control population.
Attributes
----------
att : float
Total effect on the treated, ``tau_total`` (inherited from DiDResults).
spillover_effects : pd.DataFrame, optional
Per-ring spillover-on-control estimates. Columns: ``["coef", "se",
"t_stat", "p_value", "ci_low", "ci_high"]``. Index: ring label
like ``"[0, 50)"``; for event-study output the index is a
``MultiIndex`` over ``(ring_label, event_time)``.
ring_breakpoints : list of float
Sorted distance breakpoints used to construct the rings.
d_bar : float
Far-away cutoff (Butts Assumption 5). Equals ``max(ring_breakpoints)``
unless explicitly overridden.
n_units_ever_in_ring : dict[str, int]
Counts of UNIQUE units that appear in each ring AT LEAST ONCE
across observed periods. Under staggered timing the ring
membership is time-varying, so a unit can be counted in
multiple rings (one per period it visits). Under non-staggered
timing the rings are unit-static, so this is a clean partition
(each unit appears in exactly one ring or the far-away group).
Includes treated units in Ring_1 by geometric construction
even though the ``(1 - D_it)`` factor zeros their stage-2
contribution.
n_far_away_obs : int
Number of observations with ``D_it = 0`` AND ``d_it > d_bar``;
these observations identify the counterfactual trend (Butts
Assumption 5(ii)). Under Wave E.3, on the survey path this count
is reported on the EFFECTIVE WEIGHTED ESTIMATION SAMPLE
(``count_mask = survey_finite_mask`` = finite_mask AND
``survey_weights > 0``), so zero-weight rows from
``SurveyDesign.subpopulation()`` are excluded — matches the
Wave E.3 ``n_obs`` / ``n_treated`` / ``n_control`` contract.
The upstream ``_validate_far_away_exists`` gate still fires on
the FULL DOMAIN at fit-time (Assumption 5(ii) requires at
least one far-away identifying row); this metadata only
reports the count on the active sample.
is_staggered : bool
True if multiple distinct treatment-onset times were detected.
event_study : bool
True if per-event-time ring coefficients were emitted (i.e.,
``self.event_study=True`` was set on the estimator).
stage1_n_obs : int
Number of observations in the stage-1 untreated-and-unexposed
subsample (``Omega_0_butts``).
"""
spillover_effects: Optional[pd.DataFrame] = field(default=None)
ring_breakpoints: Optional[List[float]] = field(default=None)
d_bar: Optional[float] = field(default=None)
n_units_ever_in_ring: Optional[Dict[str, int]] = field(default=None)
n_far_away_obs: Optional[int] = field(default=None)
is_staggered: Optional[bool] = field(default=None)
event_study: Optional[bool] = field(default=None)
stage1_n_obs: Optional[int] = field(default=None)
anticipation: Optional[int] = field(default=None)
# Wave C event-study fields (None when event_study=False):
att_dynamic: Optional[pd.DataFrame] = field(default=None)
# Per-event-time direct effects DataFrame indexed by integer k.
# Columns: ["coef", "se", "t_stat", "p_value", "ci_low", "ci_high", "n_obs"].
# Includes the reference period row with coef=0.0, se=0.0, n_obs=0.
event_study_effects: Optional[Dict[int, Dict[str, Any]]] = field(default=None)
# TwoStageDiD-compatible alias for ``att_dynamic`` consumable by
# ``plot_event_study`` (wired in Wave C via the ``reference_period``
# attribute fallback in ``_extract_plot_data``). ``DiagnosticReport``
# routing is NOT yet wired — registering ``SpilloverDiDResults`` in
# ``DiagnosticReport``'s applicability/method tables is a planned
# follow-up (see TODO.md).
# Schema mirrors ``two_stage.py:1355-1389``:
# {k: {"effect", "se", "n_obs", "t_stat", "p_value", "conf_int": (low, high)}}
# Reference row uses ``conf_int = (0.0, 0.0)`` (TwoStageDiD parity).
horizon_max: Optional[int] = field(default=None)
reference_period: Optional[int] = field(default=None)
# Wave E.1 survey-design fields (None when survey_design=None on fit).
# `survey_metadata` is a ``SurveyMetadata`` dataclass with DEFF /
# df_survey / n_psu / n_strata; populated only on the survey path.
# ``n_psu`` and ``n_strata`` are surfaced as top-level fields for
# convenient access without unpacking ``survey_metadata`` (parity
# with TwoStageDiDResults convention).
survey_metadata: Optional[Any] = field(default=None)
n_psu: Optional[int] = field(default=None)
n_strata: Optional[int] = field(default=None)
[docs]
def summary(self, alpha: Optional[float] = None) -> str:
"""Extended summary with ATT row, per-event-time direct block, and
per-(ring, event-time) spillover block."""
base = super().summary(alpha=alpha)
insert_blocks: List[str] = []
# Wave C event-study: per-event-time direct effects block.
if self.att_dynamic is not None and not self.att_dynamic.empty:
insert_blocks.append("")
insert_blocks.append("Dynamic Direct Effects by Event Time".center(70))
insert_blocks.append("-" * 70)
insert_blocks.append(
f"{'k':<15} {'Estimate':>12} {'Std. Err.':>12} "
f"{'t-stat':>10} {'P>|t|':>10} {'':>5}"
)
insert_blocks.append("-" * 70)
for k, row in self.att_dynamic.iterrows():
coef = row.get("coef", np.nan)
se = row.get("se", np.nan)
t_stat = row.get("t_stat", np.nan)
p_value = row.get("p_value", np.nan)
stars = _get_significance_stars(p_value)
k_str = f"{int(k):+d}"
insert_blocks.append(
f"{k_str:<15} {coef:>12.4f} {se:>12.4f} "
f"{t_stat:>10.3f} {p_value:>10.4f} {stars:>5}"
)
insert_blocks.append("-" * 70)
# Spillover block (per-ring OR per-(ring, k) under MultiIndex).
# When the index is a MultiIndex (event-study mode), the ring and `k`
# are rendered as separate columns so distinct horizons within the same
# ring remain visually distinguishable. The non-MultiIndex aggregate
# path retains the single `Ring` column for Wave B compatibility.
if self.spillover_effects is not None and not self.spillover_effects.empty:
insert_blocks.append("")
insert_blocks.append("Spillover Effects (ring-indicator, Butts 2021)".center(70))
insert_blocks.append("-" * 70)
is_multi = isinstance(self.spillover_effects.index, pd.MultiIndex)
if is_multi:
header = (
f"{'Ring':<15} {'k':>5} {'Estimate':>12} {'Std. Err.':>12} "
f"{'t-stat':>10} {'P>|t|':>10} {'':>5}"
)
else:
header = (
f"{'Ring':<15} {'Estimate':>12} {'Std. Err.':>12} "
f"{'t-stat':>10} {'P>|t|':>10} {'':>5}"
)
insert_blocks.append(header)
insert_blocks.append("-" * len(header.rstrip()))
for label, row in self.spillover_effects.iterrows():
coef = row.get("coef", np.nan)
se = row.get("se", np.nan)
t_stat = row.get("t_stat", np.nan)
p_value = row.get("p_value", np.nan)
stars = _get_significance_stars(p_value)
if is_multi and isinstance(label, tuple):
ring_str = str(label[0])[:15]
k_str = f"{int(label[1]):+d}"
insert_blocks.append(
f"{ring_str:<15} {k_str:>5} {coef:>12.4f} {se:>12.4f} "
f"{t_stat:>10.3f} {p_value:>10.4f} {stars:>5}"
)
else:
label_str = str(label)[:15]
insert_blocks.append(
f"{label_str:<15} {coef:>12.4f} {se:>12.4f} "
f"{t_stat:>10.3f} {p_value:>10.4f} {stars:>5}"
)
insert_blocks.append("-" * len(header.rstrip()))
if not insert_blocks:
return base
lines = base.split("\n")
for idx in range(len(lines) - 1, -1, -1):
if lines[idx].startswith("="):
lines = lines[:idx] + insert_blocks + lines[idx:]
break
return "\n".join(lines)
[docs]
def to_dict(self) -> Dict[str, Any]:
"""Override to serialize ``spillover_effects`` DataFrame as list-of-dicts."""
base = super().to_dict()
base.update(
{
"spillover_effects": (
self.spillover_effects.reset_index().to_dict(orient="records")
if self.spillover_effects is not None
else None
),
"ring_breakpoints": self.ring_breakpoints,
"d_bar": self.d_bar,
"n_units_ever_in_ring": self.n_units_ever_in_ring,
"n_far_away_obs": self.n_far_away_obs,
"is_staggered": self.is_staggered,
"event_study": self.event_study,
"stage1_n_obs": self.stage1_n_obs,
"anticipation": self.anticipation,
"att_dynamic": (
self.att_dynamic.reset_index().to_dict(orient="records")
if self.att_dynamic is not None
else None
),
"event_study_effects": self.event_study_effects,
"horizon_max": self.horizon_max,
"reference_period": self.reference_period,
}
)
return base
[docs]
@dataclass
class PeriodEffect:
"""
Treatment effect for a single time period.
Attributes
----------
period : any
The time period identifier.
effect : float
The treatment effect estimate for this period.
se : float
Standard error of the effect estimate.
t_stat : float
T-statistic for the effect estimate.
p_value : float
P-value for the null hypothesis that effect = 0.
conf_int : tuple[float, float]
Confidence interval for the effect.
"""
period: Any
effect: float
se: float
t_stat: float
p_value: float
conf_int: Tuple[float, float]
[docs]
def __repr__(self) -> str:
"""Concise string representation."""
sig = _get_significance_stars(self.p_value)
return (
f"PeriodEffect(period={self.period}, effect={self.effect:.4f}{sig}, "
f"SE={self.se:.4f}, p={self.p_value:.4f})"
)
@property
def is_significant(self) -> bool:
"""Check if the effect is statistically significant at 0.05 level."""
return bool(self.p_value < 0.05)
@property
def significance_stars(self) -> str:
"""Return significance stars based on p-value."""
return _get_significance_stars(self.p_value)
[docs]
@dataclass
class MultiPeriodDiDResults:
"""
Results from a Multi-Period Difference-in-Differences estimation.
Provides access to period-specific treatment effects as well as
an aggregate average treatment effect.
Attributes
----------
period_effects : dict[any, PeriodEffect]
Dictionary mapping period identifiers to their PeriodEffect objects.
Contains all estimated period effects (pre and post, excluding
the reference period which is normalized to zero).
avg_att : float
Average Treatment effect on the Treated across post-periods only.
avg_se : float
Standard error of the average ATT.
avg_t_stat : float
T-statistic for the average ATT.
avg_p_value : float
P-value for the null hypothesis that average ATT = 0.
avg_conf_int : tuple[float, float]
Confidence interval for the average ATT.
n_obs : int
Number of observations used in estimation.
n_treated : int
Number of treated units/observations.
n_control : int
Number of control units/observations.
pre_periods : list
List of pre-treatment period identifiers.
post_periods : list
List of post-treatment period identifiers.
reference_period : any, optional
The reference (omitted) period. Its coefficient is zero by
construction and it is excluded from ``period_effects``.
interaction_indices : dict, optional
Mapping from period identifier to column index in the full
variance-covariance matrix. Used internally for sub-VCV
extraction (e.g., by HonestDiD and PreTrendsPower).
"""
period_effects: Dict[Any, PeriodEffect]
avg_att: float
avg_se: float
avg_t_stat: float
avg_p_value: float
avg_conf_int: Tuple[float, float]
n_obs: int
n_treated: int
n_control: int
pre_periods: List[Any]
post_periods: List[Any]
alpha: float = 0.05
coefficients: Optional[Dict[str, float]] = field(default=None)
vcov: Optional[np.ndarray] = field(default=None)
residuals: Optional[np.ndarray] = field(default=None)
fitted_values: Optional[np.ndarray] = field(default=None)
r_squared: Optional[float] = field(default=None)
reference_period: Optional[Any] = field(default=None)
interaction_indices: Optional[Dict[Any, int]] = field(default=None, repr=False)
# Survey design metadata (SurveyMetadata instance from diff_diff.survey)
survey_metadata: Optional[Any] = field(default=None)
# Inference method (always "analytical" today for MultiPeriodDiD; included for
# symmetry with DiDResults and so summary() can gate the Variance label).
inference_method: str = field(default="analytical")
n_bootstrap: Optional[int] = field(default=None)
n_clusters: Optional[int] = field(default=None)
# Variance-covariance family and cluster column for summary() labeling.
# vcov_type='conley' on MultiPeriodDiD uses the panel block-decomposed
# form; `conley_lag_cutoff` carries the within-unit Bartlett max lag
# (matches the constructor arg). See _format_vcov_label.
vcov_type: Optional[str] = field(default=None)
cluster_name: Optional[str] = field(default=None)
conley_lag_cutoff: Optional[int] = field(default=None)
# --- Inference-field aliases (balance/external-adapter compatibility) ---
@property
def att(self) -> float:
return self.avg_att
@property
def se(self) -> float:
return self.avg_se
@property
def conf_int(self) -> Tuple[float, float]:
return self.avg_conf_int
@property
def p_value(self) -> float:
return self.avg_p_value
@property
def t_stat(self) -> float:
return self.avg_t_stat
[docs]
def __repr__(self) -> str:
"""Concise string representation."""
sig = _get_significance_stars(self.avg_p_value)
return (
f"MultiPeriodDiDResults(avg_ATT={self.avg_att:.4f}{sig}, "
f"SE={self.avg_se:.4f}, "
f"n_post_periods={len(self.post_periods)})"
)
@property
def pre_period_effects(self) -> Dict[Any, PeriodEffect]:
"""Pre-period effects only (for parallel trends assessment)."""
return {p: pe for p, pe in self.period_effects.items() if p in self.pre_periods}
@property
def post_period_effects(self) -> Dict[Any, PeriodEffect]:
"""Post-period effects only."""
return {p: pe for p, pe in self.period_effects.items() if p in self.post_periods}
@property
def coef_var(self) -> float:
"""Coefficient of variation: SE / abs(overall ATT). NaN when ATT is 0 or SE non-finite."""
if not (np.isfinite(self.avg_se) and self.avg_se >= 0):
return np.nan
if not np.isfinite(self.avg_att) or self.avg_att == 0:
return np.nan
return self.avg_se / abs(self.avg_att)
[docs]
def summary(self, alpha: Optional[float] = None) -> str:
"""
Generate a formatted summary of the estimation results.
Parameters
----------
alpha : float, optional
Significance level for confidence intervals. Defaults to the
alpha used during estimation.
Returns
-------
str
Formatted summary table.
"""
alpha = alpha or self.alpha
conf_level = int((1 - alpha) * 100)
lines = [
"=" * 80,
"Multi-Period Difference-in-Differences Estimation Results".center(80),
"=" * 80,
"",
f"{'Observations:':<25} {self.n_obs:>10}",
f"{'Treated observations:':<25} {self.n_treated:>10}",
f"{'Control observations:':<25} {self.n_control:>10}",
f"{'Pre-treatment periods:':<25} {len(self.pre_periods):>10}",
f"{'Post-treatment periods:':<25} {len(self.post_periods):>10}",
]
if self.r_squared is not None:
lines.append(f"{'R-squared:':<25} {self.r_squared:>10.4f}")
# Add survey design info
if self.survey_metadata is not None:
sm = self.survey_metadata
lines.extend(_format_survey_block(sm, 80))
# Variance family label (only when inference was analytical AND not survey).
# Survey fits use Taylor linearization or replicate-weight variance, which
# _format_survey_block already surfaces above; a parallel analytical label
# would mislabel the actual inference source.
if (
self.vcov_type is not None
and self.inference_method == "analytical"
and self.survey_metadata is None
):
label = _format_vcov_label(
self.vcov_type,
cluster_name=self.cluster_name,
n_clusters=self.n_clusters,
n_obs=self.n_obs,
conley_lag_cutoff=self.conley_lag_cutoff,
)
if label is not None:
lines.append(f"{'Variance:':<25} {label:>50}")
# Pre-period effects (parallel trends test)
pre_effects = {p: pe for p, pe in self.period_effects.items() if p in self.pre_periods}
if pre_effects:
lines.extend(
[
"",
"-" * 80,
"Pre-Period Effects (Parallel Trends Test)".center(80),
"-" * 80,
f"{'Period':<15} {'Estimate':>12} {'Std. Err.':>12} {'t-stat':>10} {'P>|t|':>10} {'Sig.':>6}",
"-" * 80,
]
)
for period in self.pre_periods:
if period in self.period_effects:
pe = self.period_effects[period]
stars = pe.significance_stars
lines.append(
f"{str(period):<15} {pe.effect:>12.4f} {pe.se:>12.4f} "
f"{pe.t_stat:>10.3f} {pe.p_value:>10.4f} {stars:>6}"
)
# Show reference period
if self.reference_period is not None:
lines.append(
f"[ref: {self.reference_period}]"
f"{'0.0000':>21} {'---':>12} {'---':>10} {'---':>10} {'':>6}"
)
lines.append("-" * 80)
# Post-period treatment effects
lines.extend(
[
"",
"-" * 80,
"Post-Period Treatment Effects".center(80),
"-" * 80,
f"{'Period':<15} {'Estimate':>12} {'Std. Err.':>12} {'t-stat':>10} {'P>|t|':>10} {'Sig.':>6}",
"-" * 80,
]
)
for period in self.post_periods:
pe = self.period_effects[period]
stars = pe.significance_stars
lines.append(
f"{str(period):<15} {pe.effect:>12.4f} {pe.se:>12.4f} "
f"{pe.t_stat:>10.3f} {pe.p_value:>10.4f} {stars:>6}"
)
# Average effect
lines.extend(
[
"-" * 80,
"",
"-" * 80,
"Average Treatment Effect (across post-periods)".center(80),
"-" * 80,
f"{'Parameter':<15} {'Estimate':>12} {'Std. Err.':>12} {'t-stat':>10} {'P>|t|':>10} {'Sig.':>6}",
"-" * 80,
f"{'Avg ATT':<15} {self.avg_att:>12.4f} {self.avg_se:>12.4f} "
f"{self.avg_t_stat:>10.3f} {self.avg_p_value:>10.4f} {self.significance_stars:>6}",
"-" * 80,
"",
f"{conf_level}% Confidence Interval: [{self.avg_conf_int[0]:.4f}, {self.avg_conf_int[1]:.4f}]",
]
)
cv = self.coef_var
if np.isfinite(cv):
lines.append(f"{'CV (SE/abs(ATT)):':<25} {cv:>10.4f}")
# Add significance codes
lines.extend(
[
"",
"Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1",
"=" * 80,
]
)
return "\n".join(lines)
[docs]
def print_summary(self, alpha: Optional[float] = None) -> None:
"""Print the summary to stdout."""
print(self.summary(alpha))
[docs]
def get_effect(self, period) -> PeriodEffect:
"""
Get the treatment effect for a specific period.
Parameters
----------
period : any
The period identifier.
Returns
-------
PeriodEffect
The treatment effect for the specified period.
Raises
------
KeyError
If the period is not found in post-treatment periods.
"""
if period not in self.period_effects:
if hasattr(self, "reference_period") and period == self.reference_period:
raise KeyError(
f"Period '{period}' is the reference period (coefficient "
f"normalized to zero by construction). Its effect is 0.0 with "
f"no associated uncertainty."
)
raise KeyError(
f"Period '{period}' not found. "
f"Available periods: {list(self.period_effects.keys())}"
)
return self.period_effects[period]
[docs]
def to_dict(self) -> Dict[str, Any]:
"""
Convert results to a dictionary.
Returns
-------
Dict[str, Any]
Dictionary containing all estimation results.
"""
result: Dict[str, Any] = {
"avg_att": self.avg_att,
"avg_se": self.avg_se,
"avg_t_stat": self.avg_t_stat,
"avg_p_value": self.avg_p_value,
"avg_conf_int_lower": self.avg_conf_int[0],
"avg_conf_int_upper": self.avg_conf_int[1],
"n_obs": self.n_obs,
"n_treated": self.n_treated,
"n_control": self.n_control,
"n_pre_periods": len(self.pre_periods),
"n_post_periods": len(self.post_periods),
"r_squared": self.r_squared,
"reference_period": self.reference_period,
}
# Variance-family metadata: included only when set, so existing
# dict consumers see no new keys for non-conley / non-cluster fits.
if self.vcov_type is not None:
result["vcov_type"] = self.vcov_type
if self.cluster_name is not None:
result["cluster_name"] = self.cluster_name
if self.conley_lag_cutoff is not None:
result["conley_lag_cutoff"] = self.conley_lag_cutoff
# Add period-specific effects
for period, pe in self.period_effects.items():
result[f"effect_period_{period}"] = pe.effect
result[f"se_period_{period}"] = pe.se
result[f"pval_period_{period}"] = pe.p_value
# Add survey metadata if present
if self.survey_metadata is not None:
sm = self.survey_metadata
result["weight_type"] = sm.weight_type
result["effective_n"] = sm.effective_n
result["design_effect"] = sm.design_effect
result["sum_weights"] = sm.sum_weights
result["n_strata"] = sm.n_strata
result["n_psu"] = sm.n_psu
result["df_survey"] = sm.df_survey
return result
[docs]
def to_dataframe(self) -> pd.DataFrame:
"""
Convert period-specific effects to a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with one row per estimated period (pre and post).
"""
rows = []
for period, pe in self.period_effects.items():
rows.append(
{
"period": period,
"effect": pe.effect,
"se": pe.se,
"t_stat": pe.t_stat,
"p_value": pe.p_value,
"conf_int_lower": pe.conf_int[0],
"conf_int_upper": pe.conf_int[1],
"is_significant": pe.is_significant,
"is_post": period in self.post_periods,
}
)
return pd.DataFrame(rows)
@property
def is_significant(self) -> bool:
"""Check if the average ATT is statistically significant at the alpha level."""
return bool(self.avg_p_value < self.alpha)
@property
def significance_stars(self) -> str:
"""Return significance stars for the average ATT based on p-value."""
return _get_significance_stars(self.avg_p_value)
@dataclass
class _SyntheticDiDFitSnapshot:
"""Internal snapshot of fit() state retained on SyntheticDiDResults for
post-hoc diagnostic methods (in-time placebo, regularization sensitivity).
Not part of the public API. Arrays are marked read-only after
construction to prevent accidental mutation by diagnostic methods.
Memory scales as O(n_units * n_periods).
"""
Y_pre_control: np.ndarray
Y_post_control: np.ndarray
Y_pre_treated: np.ndarray
Y_post_treated: np.ndarray
control_unit_ids: List[Any]
treated_unit_ids: List[Any]
pre_periods: List[Any]
post_periods: List[Any]
w_control: Optional[np.ndarray] = None
w_treated: Optional[np.ndarray] = None
# Normalization constants captured during fit() so diagnostic methods can
# reproduce the main path's centering+scaling and avoid catastrophic
# cancellation on extreme-Y panels. Defaults preserve behavior for
# snapshots built before these fields existed.
Y_shift: float = 0.0
Y_scale: float = 1.0
def __post_init__(self):
for arr in (
self.Y_pre_control,
self.Y_post_control,
self.Y_pre_treated,
self.Y_post_treated,
):
arr.setflags(write=False)
for arr in (self.w_control, self.w_treated):
if arr is not None:
arr.setflags(write=False)
[docs]
@dataclass
class SyntheticDiDResults:
"""
Results from a Synthetic Difference-in-Differences estimation.
Combines DiD with synthetic control by re-weighting control units to match
pre-treatment trends of treated units.
Attributes
----------
att : float
Average Treatment effect on the Treated (ATT).
se : float
Standard error of the ATT estimate (bootstrap, jackknife, or placebo-based).
t_stat : float
T-statistic for the ATT estimate.
p_value : float
P-value for the null hypothesis that ATT = 0.
conf_int : tuple[float, float]
Confidence interval for the ATT.
n_obs : int
Number of observations used in estimation.
n_treated : int
Number of treated units/observations.
n_control : int
Number of control units/observations.
unit_weights : dict
Dictionary mapping control unit IDs to their synthetic weights.
When survey weights are used, these are the composed effective
weights (omega_eff = raw Frank-Wolfe * survey, renormalized) that
were applied to produce the ATT, not the raw Frank-Wolfe solution.
time_weights : dict
Dictionary mapping pre-treatment periods to their time weights.
pre_periods : list
List of pre-treatment period identifiers.
post_periods : list
List of post-treatment period identifiers.
variance_method : str
Method used for variance estimation: ``"bootstrap"`` (paper-faithful
pairs bootstrap re-estimating ω and λ via Frank-Wolfe on each draw;
Arkhangelsky et al. 2021 Algorithm 2 step 2, and R's default
``synthdid::vcov(method="bootstrap")``), ``"jackknife"``, or
``"placebo"``.
placebo_effects : np.ndarray, optional
Method-specific per-iteration estimates: placebo treatment effects
(for ``"placebo"``), bootstrap ATT estimates with re-estimated
weights per draw (for ``"bootstrap"``), or leave-one-out estimates
(for ``"jackknife"``). The ``variance_method`` field disambiguates
the contents.
synthetic_pre_trajectory : np.ndarray, optional
Synthetic control trajectory in pre-treatment periods, shape
``(n_pre,)``. Equal to ``Y_pre_control @ omega_eff`` where
``omega_eff`` is the composed effective weight vector.
synthetic_post_trajectory : np.ndarray, optional
Synthetic control trajectory in post-treatment periods, shape
``(n_post,)``.
treated_pre_trajectory : np.ndarray, optional
Treated-unit mean trajectory in pre-treatment periods, shape
``(n_pre,)``. Survey-weighted when the fit used survey weights.
treated_post_trajectory : np.ndarray, optional
Treated-unit mean trajectory in post-treatment periods, shape
``(n_post,)``.
time_weights_array : np.ndarray, optional
The Frank-Wolfe time weights as a 1-D array (same values as the
``time_weights`` dict but order-stable and usable for re-estimation
by sensitivity methods). Shape ``(n_pre,)``.
"""
att: float
se: float
t_stat: float
p_value: float
conf_int: Tuple[float, float]
n_obs: int
n_treated: int
n_control: int
unit_weights: Dict[Any, float]
time_weights: Dict[Any, float]
pre_periods: List[Any]
post_periods: List[Any]
alpha: float = 0.05
variance_method: str = field(default="placebo")
noise_level: Optional[float] = field(default=None)
zeta_omega: Optional[float] = field(default=None)
zeta_lambda: Optional[float] = field(default=None)
pre_treatment_fit: Optional[float] = field(default=None)
placebo_effects: Optional[np.ndarray] = field(default=None)
n_bootstrap: Optional[int] = field(default=None)
# Survey design metadata (SurveyMetadata instance from diff_diff.survey)
survey_metadata: Optional[Any] = field(default=None)
# Trajectory data for plotting / custom fit metrics
synthetic_pre_trajectory: Optional[np.ndarray] = field(default=None)
synthetic_post_trajectory: Optional[np.ndarray] = field(default=None)
treated_pre_trajectory: Optional[np.ndarray] = field(default=None)
treated_post_trajectory: Optional[np.ndarray] = field(default=None)
# Frank-Wolfe time weights as ndarray, needed by sensitivity_to_zeta_omega
# which holds time weights fixed
time_weights_array: Optional[np.ndarray] = field(default=None)
# Internal diagnostic state attached by SyntheticDiD.fit() after
# construction as plain instance attributes (NOT dataclass fields). Kept
# out of the field list deliberately so dataclass-recursive serializers
# (dataclasses.asdict, dataclasses.fields, dataclasses.replace) cannot
# reach the retained panel state or unit IDs. See __post_init__ for
# None-initialization; SyntheticDiD.fit() populates.
def __post_init__(self):
# Plain attributes rather than dataclass fields so asdict()-style
# recursion cannot serialize internal panel state.
self._loo_unit_ids: Optional[List[Any]] = None
# Granularity of the `placebo_effects` LOO array: "unit" (non-
# survey + pweight-only jackknife), "psu" (full-design survey
# jackknife), or None (non-jackknife variance methods). Governs
# which accessors are well-defined. Set by `fit()` at result
# construction time.
self._loo_granularity: Optional[str] = None
self._loo_roles: Optional[List[str]] = None
self._fit_snapshot: Optional[_SyntheticDiDFitSnapshot] = None
[docs]
def __repr__(self) -> str:
"""Concise string representation."""
sig = _get_significance_stars(self.p_value)
return (
f"SyntheticDiDResults(ATT={self.att:.4f}{sig}, "
f"SE={self.se:.4f}, "
f"p={self.p_value:.4f})"
)
[docs]
def __getstate__(self) -> Dict[str, Any]:
"""Exclude the internal fit snapshot from pickling.
The snapshot retains outcome matrices, unit IDs, and survey weights
to support post-hoc diagnostics (`in_time_placebo`,
`sensitivity_to_zeta_omega`). Serialization would otherwise carry
that panel state to wherever the pickle is sent, which is a privacy
hazard for survey-weighted or sensitive fits.
Unpickled results keep the public fields (ATT, weights, trajectories,
etc.); calling a diagnostic method that needs the snapshot raises a
ValueError directing the user to re-fit.
"""
state = self.__dict__.copy()
state["_fit_snapshot"] = None
return state
@property
def coef_var(self) -> float:
"""Coefficient of variation: SE / abs(ATT). NaN when ATT is 0 or SE non-finite."""
if not (np.isfinite(self.se) and self.se >= 0):
return np.nan
if not np.isfinite(self.att) or self.att == 0:
return np.nan
return self.se / abs(self.att)
[docs]
def summary(self, alpha: Optional[float] = None) -> str:
"""
Generate a formatted summary of the estimation results.
Parameters
----------
alpha : float, optional
Significance level for confidence intervals. Defaults to the
alpha used during estimation.
Returns
-------
str
Formatted summary table.
"""
alpha = alpha or self.alpha
conf_level = int((1 - alpha) * 100)
lines = [
"=" * 75,
"Synthetic Difference-in-Differences Estimation Results".center(75),
"=" * 75,
"",
f"{'Observations:':<25} {self.n_obs:>10}",
f"{'Treated:':<25} {self.n_treated:>10}",
f"{'Control:':<25} {self.n_control:>10}",
f"{'Pre-treatment periods:':<25} {len(self.pre_periods):>10}",
f"{'Post-treatment periods:':<25} {len(self.post_periods):>10}",
]
if self.zeta_omega is not None:
lines.append(f"{'Zeta (unit weights):':<25} {self.zeta_omega:>10.4f}")
if self.zeta_lambda is not None:
lines.append(f"{'Zeta (time weights):':<25} {self.zeta_lambda:>10.6f}")
if self.noise_level is not None:
lines.append(f"{'Noise level:':<25} {self.noise_level:>10.4f}")
if self.pre_treatment_fit is not None:
lines.append(f"{'Pre-treatment fit (RMSE):':<25} {self.pre_treatment_fit:>10.4f}")
# Variance method info
lines.append(f"{'Variance method:':<25} {self.variance_method:>10}")
if self.variance_method == "bootstrap" and self.n_bootstrap is not None:
lines.append(f"{'Bootstrap replications:':<25} {self.n_bootstrap:>10}")
# Add survey design info
if self.survey_metadata is not None:
sm = self.survey_metadata
lines.extend(_format_survey_block(sm, 75))
lines.extend(
[
"",
"-" * 75,
f"{'Parameter':<15} {'Estimate':>12} {'Std. Err.':>12} {'t-stat':>10} {'P>|t|':>10} {'':>5}",
"-" * 75,
f"{'ATT':<15} {self.att:>12.4f} {self.se:>12.4f} {self.t_stat:>10.3f} {self.p_value:>10.4f} {self.significance_stars:>5}",
"-" * 75,
"",
f"{conf_level}% Confidence Interval: [{self.conf_int[0]:.4f}, {self.conf_int[1]:.4f}]",
]
)
cv = self.coef_var
if np.isfinite(cv):
lines.append(f"{'CV (SE/abs(ATT)):':<25} {cv:>10.4f}")
# Show top unit weights
if self.unit_weights:
sorted_weights = sorted(self.unit_weights.items(), key=lambda x: x[1], reverse=True)
top_n = min(5, len(sorted_weights))
lines.extend(
[
"",
"-" * 75,
"Top Unit Weights (Synthetic Control)".center(75),
"-" * 75,
]
)
for unit, weight in sorted_weights[:top_n]:
if weight > 0.001: # Only show meaningful weights
lines.append(f" Unit {unit}: {weight:.4f}")
# Show how many units have non-trivial weight
n_nonzero = sum(1 for w in self.unit_weights.values() if w > 0.001)
lines.append(f" ({n_nonzero} units with weight > 0.001)")
# Add significance codes
lines.extend(
[
"",
"Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1",
"=" * 75,
]
)
return "\n".join(lines)
[docs]
def print_summary(self, alpha: Optional[float] = None) -> None:
"""Print the summary to stdout."""
print(self.summary(alpha))
[docs]
def to_dict(self) -> Dict[str, Any]:
"""
Convert results to a dictionary.
Returns
-------
Dict[str, Any]
Dictionary containing all estimation results.
"""
result = {
"att": self.att,
"se": self.se,
"t_stat": self.t_stat,
"p_value": self.p_value,
"conf_int_lower": self.conf_int[0],
"conf_int_upper": self.conf_int[1],
"n_obs": self.n_obs,
"n_treated": self.n_treated,
"n_control": self.n_control,
"n_pre_periods": len(self.pre_periods),
"n_post_periods": len(self.post_periods),
"variance_method": self.variance_method,
"noise_level": self.noise_level,
"zeta_omega": self.zeta_omega,
"zeta_lambda": self.zeta_lambda,
"pre_treatment_fit": self.pre_treatment_fit,
}
if self.n_bootstrap is not None:
result["n_bootstrap"] = self.n_bootstrap
if self.survey_metadata is not None:
sm = self.survey_metadata
result["weight_type"] = sm.weight_type
result["effective_n"] = sm.effective_n
result["design_effect"] = sm.design_effect
result["sum_weights"] = sm.sum_weights
result["n_strata"] = sm.n_strata
result["n_psu"] = sm.n_psu
result["df_survey"] = sm.df_survey
return result
[docs]
def to_dataframe(self) -> pd.DataFrame:
"""
Convert results to a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with estimation results.
"""
return pd.DataFrame([self.to_dict()])
[docs]
def get_unit_weights_df(self) -> pd.DataFrame:
"""
Get unit weights as a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with unit IDs and their weights.
"""
return pd.DataFrame(
[{"unit": unit, "weight": weight} for unit, weight in self.unit_weights.items()]
).sort_values("weight", ascending=False)
[docs]
def get_time_weights_df(self) -> pd.DataFrame:
"""
Get time weights as a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with time periods and their weights.
"""
return pd.DataFrame(
[{"period": period, "weight": weight} for period, weight in self.time_weights.items()]
)
[docs]
def get_loo_effects_df(self) -> pd.DataFrame:
"""
Per-unit leave-one-out ATT from the jackknife variance pass.
Requires ``variance_method='jackknife'`` (``ValueError`` otherwise)
and unit-level LOO granularity (``NotImplementedError`` for the
full-design survey jackknife path, which uses PSU-level LOO).
Available on:
* non-survey jackknife fits (classical Arkhangelsky Algorithm 3).
* pweight-only survey jackknife fits (Algorithm 3 with post-hoc
ω_eff composition; PSU labels in ``survey_metadata`` come from
implicit-PSU metadata but the LOO remains unit-level).
Blocked on:
* full-design survey jackknife fits (strata / PSU / FPC set in
``SurveyDesign``) - the underlying replicates are PSU-level
``τ̂_{(h,j)}`` (Rust & Rao 1996), not unit-level. See
``result.placebo_effects`` for the raw PSU-level replicate
array and REGISTRY §SyntheticDiD "Note (survey + jackknife
composition)" for the aggregation formula.
The underlying unit-level values come from the jackknife loops in
``SyntheticDiD._jackknife_se``: control LOO estimates fill the
first ``n_control`` positions (in the order of the control units
seen by fit), then treated LOO estimates fill the next
``n_treated`` positions. This method joins those estimates back
to user-facing unit identities.
``att_loo`` is NaN when the fit hit the zero-sum weight guard for
that unit (survey weights composed to zero once the unit was
dropped). ``delta_from_full`` propagates NaN in that case.
Returns
-------
pd.DataFrame
Columns:
- ``unit`` - user's unit ID
- ``role`` - ``'control'`` or ``'treated'``
- ``att_loo`` - ATT with this unit dropped
- ``delta_from_full`` - ``att_loo - self.att``
Sorted by ``|delta_from_full|`` descending, NaN rows at the end.
"""
if self.variance_method != "jackknife":
raise ValueError(
"get_loo_effects_df() requires variance_method='jackknife'. "
f"This result used variance_method='{self.variance_method}'. "
"Re-fit with SyntheticDiD(variance_method='jackknife') to "
"obtain per-unit leave-one-out estimates."
)
# Survey-jackknife fits use PSU-level LOO (Rust & Rao 1996) with
# stratum aggregation rather than unit-level LOO. The returned
# ``placebo_effects`` array in that path is a flat list of
# PSU-level τ̂_{(h,j)} replicates (variable length, ordered by
# stratum then PSU), not a length-N unit-indexed array. Mapping
# these onto the fit-time unit IDs would mislabel PSU replicates
# as unit effects. Block the accessor when the explicit
# granularity flag set by ``fit()`` is "psu". We key off the
# granularity flag rather than ``survey_metadata.n_psu`` because
# pweight-only survey jackknife fits also populate ``n_psu`` via
# implicit-PSU metadata (``survey.py`` L749-L753) but still run
# unit-level LOO, so the ``n_psu`` heuristic would false-positive.
if getattr(self, "_loo_granularity", None) == "psu":
raise NotImplementedError(
"get_loo_effects_df() is unit-level-LOO only. This fit used "
"the full-design survey jackknife (PSU-level LOO with "
"stratum aggregation, Rust & Rao 1996); the underlying "
"replicates are PSU-level, not unit-level, so joining them "
"back to fit-time unit IDs is not well-defined. See "
"``result.placebo_effects`` for the raw PSU-level replicate "
"array and ``docs/methodology/REGISTRY.md`` §SyntheticDiD "
'"Note (survey + jackknife composition)" for the '
"aggregation formula."
)
if self._loo_unit_ids is None or self._loo_roles is None or self.placebo_effects is None:
raise ValueError(
"Leave-one-out estimates are unavailable (jackknife returned "
"NaN or an empty array). See prior warnings from fit() for the "
"cause (e.g., single treated unit, all weight on one control)."
)
att_loo = np.asarray(self.placebo_effects, dtype=float)
delta = att_loo - self.att
df = pd.DataFrame(
{
"unit": self._loo_unit_ids,
"role": self._loo_roles,
"att_loo": att_loo,
"delta_from_full": delta,
}
)
# Sort by |delta| descending. NaN rows sort to the end so the most
# influential real units appear first.
df["_abs_delta"] = df["delta_from_full"].abs()
df = df.sort_values(by="_abs_delta", ascending=False, na_position="last").drop(
columns="_abs_delta"
)
df = df.reset_index(drop=True)
return df
[docs]
def in_time_placebo(
self,
fake_treatment_periods: Optional[List[Any]] = None,
zeta_omega_override: Optional[float] = None,
zeta_lambda_override: Optional[float] = None,
) -> pd.DataFrame:
"""
Re-estimate the ATT on shifted fake treatment periods within the
original pre-treatment window.
A credible placebo should produce near-zero ATTs at every shifted
date. Departures from zero signal that whatever the estimator
picked up at the real treatment date is also present pre-treatment,
weakening the causal interpretation.
The post-treatment data is never used — only the pre-window is
re-sliced. Regularization reuses ``self.zeta_omega`` and
``self.zeta_lambda`` from the original fit (R ``synthdid``
convention), unless overrides are supplied.
Parameters
----------
fake_treatment_periods : list, optional
Explicit pre-period values to test. If ``None`` (default),
sweeps every feasible pre-period — every P in ``pre_periods``
whose position ``i`` satisfies ``i >= 2`` (so at least 2
pre-fake periods remain for weight estimation) and
``i <= n_pre - 1`` (so at least 1 post-fake period exists).
Values not in ``pre_periods`` raise ValueError (a value in
``post_periods`` is explicitly not a placebo).
zeta_omega_override : float, optional
Override ``self.zeta_omega`` for the refit. Default reuses
the original.
zeta_lambda_override : float, optional
Override ``self.zeta_lambda`` for the refit.
Returns
-------
pd.DataFrame
Columns:
- ``fake_treatment_period`` — the shifted date
- ``att`` — placebo ATT (ideally near 0)
- ``pre_fit_rmse`` — RMSE on the fake pre-window
- ``n_pre_fake`` — periods before the fake date
- ``n_post_fake`` — periods from the fake date onward
NaN is emitted only for dimensional infeasibility. Frank-Wolfe
does not expose a mid-solver non-convergence signal; inspect
``pre_fit_rmse`` for poor refit quality.
"""
if self._fit_snapshot is None:
raise ValueError(
"in_time_placebo() requires the fit snapshot on the results "
"object. This result appears to have been loaded from "
"serialization (which excludes the snapshot) or was produced "
"by an older estimator version. Re-fit to enable."
)
from diff_diff.utils import (
compute_sdid_estimator,
compute_sdid_unit_weights,
compute_time_weights,
)
snap = self._fit_snapshot
pre_periods = snap.pre_periods
n_pre = len(pre_periods)
zeta_omega = zeta_omega_override if zeta_omega_override is not None else self.zeta_omega
zeta_lambda = zeta_lambda_override if zeta_lambda_override is not None else self.zeta_lambda
if zeta_omega is None or zeta_lambda is None:
raise ValueError(
"in_time_placebo() needs zeta_omega and zeta_lambda from the "
"original fit. Expected on the results object but found None."
)
# Reproduce the main fit path's Y normalization (Y → (Y - shift) / scale)
# so Frank-Wolfe sees the same ~O(1) inputs it saw during fit() and the
# SDID double-difference does not suffer ~6-digit cancellation at
# extreme Y. See SyntheticDiD.fit() and REGISTRY.md §SyntheticDiD.
Y_shift = snap.Y_shift
Y_scale = snap.Y_scale
zeta_omega_n = zeta_omega / Y_scale
zeta_lambda_n = zeta_lambda / Y_scale
noise_level = self.noise_level if self.noise_level is not None else 0.0
noise_level_n = noise_level / Y_scale
min_decrease = 1e-5 * noise_level_n if noise_level_n > 0 else 1e-5
# Build the list of (fake_period, position) pairs to iterate.
period_to_idx = {p: i for i, p in enumerate(pre_periods)}
if fake_treatment_periods is None:
positions = list(range(2, n_pre))
fake_list = [(pre_periods[i], i) for i in positions]
else:
fake_list = []
for p in fake_treatment_periods:
if p in snap.post_periods:
raise ValueError(
f"fake_treatment_period={p!r} is in post_periods; a real "
"treatment date is not a placebo. Choose a value from "
"pre_periods."
)
if p not in period_to_idx:
raise ValueError(
f"fake_treatment_period={p!r} not found in pre_periods "
f"({pre_periods!r})."
)
fake_list.append((p, period_to_idx[p]))
columns = [
"fake_treatment_period",
"att",
"pre_fit_rmse",
"n_pre_fake",
"n_post_fake",
]
if not fake_list:
return pd.DataFrame(columns=columns)
rows: List[Dict[str, Any]] = []
for fake_period, i in fake_list:
n_pre_fake = i
n_post_fake = n_pre - i
row: Dict[str, Any] = {
"fake_treatment_period": fake_period,
"att": float("nan"),
"pre_fit_rmse": float("nan"),
"n_pre_fake": n_pre_fake,
"n_post_fake": n_post_fake,
}
# Dimensional infeasibility: Frank-Wolfe needs >=2 pre-fake
# periods for unit weights; the estimator needs >=1 post-fake.
if n_pre_fake < 2 or n_post_fake < 1:
rows.append(row)
continue
Y_pre_c_n = (snap.Y_pre_control[:i, :] - Y_shift) / Y_scale
Y_post_c_n = (snap.Y_pre_control[i:, :] - Y_shift) / Y_scale
Y_pre_t_n = (snap.Y_pre_treated[:i, :] - Y_shift) / Y_scale
Y_post_t_n = (snap.Y_pre_treated[i:, :] - Y_shift) / Y_scale
if snap.w_treated is not None:
w_t = snap.w_treated
y_pre_t_mean_n = np.average(Y_pre_t_n, axis=1, weights=w_t)
y_post_t_mean_n = np.average(Y_post_t_n, axis=1, weights=w_t)
else:
y_pre_t_mean_n = np.mean(Y_pre_t_n, axis=1)
y_post_t_mean_n = np.mean(Y_post_t_n, axis=1)
omega_fake = compute_sdid_unit_weights(
Y_pre_c_n,
y_pre_t_mean_n,
zeta_omega=zeta_omega_n,
min_decrease=min_decrease,
)
lambda_fake = compute_time_weights(
Y_pre_c_n,
Y_post_c_n,
zeta_lambda=zeta_lambda_n,
min_decrease=min_decrease,
)
if snap.w_control is not None:
omega_eff_fake = omega_fake * snap.w_control
denom = omega_eff_fake.sum()
if denom == 0:
rows.append(row)
continue
omega_eff_fake = omega_eff_fake / denom
else:
omega_eff_fake = omega_fake
att_fake_n = compute_sdid_estimator(
Y_pre_c_n,
Y_post_c_n,
y_pre_t_mean_n,
y_post_t_mean_n,
omega_eff_fake,
lambda_fake,
)
synthetic_pre_fake_n = Y_pre_c_n @ omega_eff_fake
pre_fit_n = float(np.sqrt(np.mean((y_pre_t_mean_n - synthetic_pre_fake_n) ** 2)))
# ATT is scale-equivariant and shift-invariant in Y; RMSE is
# scale-equivariant. Rescale back to original-Y units.
row["att"] = float(att_fake_n * Y_scale)
row["pre_fit_rmse"] = pre_fit_n * Y_scale
rows.append(row)
return pd.DataFrame(rows)
[docs]
def sensitivity_to_zeta_omega(
self,
zeta_grid: Optional[List[float]] = None,
multipliers: Tuple[float, ...] = (0.25, 0.5, 1.0, 2.0, 4.0),
) -> pd.DataFrame:
"""
Re-estimate the ATT across a grid of ``zeta_omega`` values to show
how sensitive the estimate is to the unit-weight regularization.
The Frank-Wolfe time weights computed during the original fit are
held fixed here — this method isolates sensitivity to
``zeta_omega`` specifically. ``zeta_lambda`` and the time weights
are not re-fit.
Parameters
----------
zeta_grid : list of float, optional
Absolute ``zeta_omega`` values to evaluate. If ``None``
(default), uses ``multipliers * self.zeta_omega`` — i.e. a
5-point grid by default, spanning 16x from the smallest to
the largest multiplier and symmetric in log space around 1.0.
multipliers : tuple of float, default ``(0.25, 0.5, 1.0, 2.0, 4.0)``
Multipliers on ``self.zeta_omega``. Ignored when
``zeta_grid`` is supplied.
Returns
-------
pd.DataFrame
Columns:
- ``zeta_omega`` — the regularization value evaluated
- ``att`` — resulting ATT
- ``pre_fit_rmse`` — RMSE on the original pre-period
- ``max_unit_weight`` — max element of the composed
``omega_eff`` (sensitivity indicator: close to 1 means
near-one-hot solutions; close to ``1/n_control`` means
near-uniform)
- ``effective_n`` — ``1 / sum(omega_eff**2)``
Notes
-----
Extreme ``zeta_omega``: very small values push weights toward
sparse one-hot solutions (few controls dominate); very large
values push toward uniform weighting. The ``pre_fit_rmse`` column
exposes the tradeoff.
"""
if self._fit_snapshot is None:
raise ValueError(
"sensitivity_to_zeta_omega() requires the fit snapshot on the "
"results object. This result appears to have been loaded from "
"serialization (which excludes the snapshot) or was produced "
"by an older estimator version. Re-fit to enable."
)
if self.time_weights_array is None:
raise ValueError(
"sensitivity_to_zeta_omega() needs the original time weights "
"array. Expected on the results object but found None. Re-fit "
"to populate."
)
from diff_diff.utils import compute_sdid_estimator, compute_sdid_unit_weights
snap = self._fit_snapshot
if zeta_grid is None:
if self.zeta_omega is None:
raise ValueError(
"Cannot build default zeta_grid: self.zeta_omega is None. "
"Supply zeta_grid explicitly."
)
zeta_values: List[float] = [float(m * self.zeta_omega) for m in multipliers]
else:
zeta_values = [float(z) for z in zeta_grid]
# Reproduce the main fit path's Y normalization so FW sees ~O(1)
# inputs; see in_time_placebo() for the same pattern.
Y_shift = snap.Y_shift
Y_scale = snap.Y_scale
noise_level = self.noise_level if self.noise_level is not None else 0.0
noise_level_n = noise_level / Y_scale
min_decrease = 1e-5 * noise_level_n if noise_level_n > 0 else 1e-5
Y_pre_control_n = (snap.Y_pre_control - Y_shift) / Y_scale
Y_post_control_n = (snap.Y_post_control - Y_shift) / Y_scale
Y_pre_treated_n = (snap.Y_pre_treated - Y_shift) / Y_scale
Y_post_treated_n = (snap.Y_post_treated - Y_shift) / Y_scale
if snap.w_treated is not None:
y_pre_t_mean_n = np.average(Y_pre_treated_n, axis=1, weights=snap.w_treated)
y_post_t_mean_n = np.average(Y_post_treated_n, axis=1, weights=snap.w_treated)
else:
y_pre_t_mean_n = np.mean(Y_pre_treated_n, axis=1)
y_post_t_mean_n = np.mean(Y_post_treated_n, axis=1)
columns = [
"zeta_omega",
"att",
"pre_fit_rmse",
"max_unit_weight",
"effective_n",
]
if not zeta_values:
return pd.DataFrame(columns=columns)
time_weights = np.asarray(self.time_weights_array, dtype=float)
rows: List[Dict[str, Any]] = []
for z in zeta_values:
omega_fake = compute_sdid_unit_weights(
Y_pre_control_n,
y_pre_t_mean_n,
zeta_omega=z / Y_scale,
min_decrease=min_decrease,
)
if snap.w_control is not None:
omega_eff = omega_fake * snap.w_control
denom = omega_eff.sum()
if denom == 0:
rows.append(
{
"zeta_omega": z,
"att": float("nan"),
"pre_fit_rmse": float("nan"),
"max_unit_weight": float("nan"),
"effective_n": float("nan"),
}
)
continue
omega_eff = omega_eff / denom
else:
omega_eff = omega_fake
att_n = compute_sdid_estimator(
Y_pre_control_n,
Y_post_control_n,
y_pre_t_mean_n,
y_post_t_mean_n,
omega_eff,
time_weights,
)
synthetic_pre_n = Y_pre_control_n @ omega_eff
pre_fit_n = float(np.sqrt(np.mean((y_pre_t_mean_n - synthetic_pre_n) ** 2)))
herf = float(np.sum(omega_eff**2))
rows.append(
{
"zeta_omega": z,
# Unit weights are scale-invariant; ATT and RMSE are
# scale-equivariant. Report original-Y units.
"att": float(att_n * Y_scale),
"pre_fit_rmse": pre_fit_n * Y_scale,
"max_unit_weight": float(np.max(omega_eff)),
"effective_n": float("nan") if herf == 0 else 1.0 / herf,
}
)
return pd.DataFrame(rows, columns=columns)
[docs]
def get_weight_concentration(self, top_k: int = 5) -> Dict[str, Any]:
"""
Concentration metrics for the control unit weights.
Operates on ``self.unit_weights``, which for survey-weighted fits
stores the composed effective weights
(``omega_eff = raw_omega * w_control``, renormalized to sum to 1)
that were applied to produce the ATT. For non-survey fits the
values equal the raw Frank-Wolfe solution. Either way, the
concentration reflects the distribution actually used by the
estimator.
Parameters
----------
top_k : int, default 5
Number of largest weights to sum for ``top_k_share``. Must be
non-negative. Clamped to the available number of control units.
Returns
-------
dict
Keys:
- ``effective_n`` — ``1 / sum(w**2)``, inverse Herfindahl
- ``herfindahl`` — ``sum(w**2)``
- ``top_k_share`` — sum of the ``top_k`` largest weights
- ``top_k`` — the (possibly clamped) value used
Raises
------
ValueError
If ``top_k`` is negative.
"""
if top_k < 0:
raise ValueError(f"top_k must be non-negative (got {top_k}).")
weights = np.asarray(list(self.unit_weights.values()), dtype=float)
if weights.size == 0:
return {
"effective_n": float("nan"),
"herfindahl": float("nan"),
"top_k_share": float("nan"),
"top_k": 0,
}
herfindahl = float(np.sum(weights**2))
effective_n = float("nan") if herfindahl == 0 else 1.0 / herfindahl
k = min(int(top_k), weights.size)
if k <= 0:
top_k_share = 0.0
else:
top_k_share = float(np.sort(weights)[-k:].sum())
return {
"effective_n": effective_n,
"herfindahl": herfindahl,
"top_k_share": top_k_share,
"top_k": k,
}
@property
def is_significant(self) -> bool:
"""Check if the ATT is statistically significant at the alpha level."""
return bool(self.p_value < self.alpha)
@property
def significance_stars(self) -> str:
"""Return significance stars based on p-value."""
return _get_significance_stars(self.p_value)