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

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"""

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],

) -> 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). vcov_type='conley' is intentionally

not labeled here: DifferenceInDifferences / MultiPeriodDiD / TwoWayFixedEffects

all reject vcov_type='conley' at fit-time (Phase 1 supports cross-sectional

Conley only via direct compute_robust_vcov / LinearRegression), so a

Conley label cannot be reached on these result classes.

"""

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})"

return None

@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".

# Plus cluster_name when cluster-robust. Used by summary() to label the

# SE family in the output. vcov_type='conley' is rejected at fit-time

# for all panel estimators (DifferenceInDifferences/MultiPeriodDiD/TWFE)

# in Phase 1; the supported Conley path is direct LinearRegression /

# compute_robust_vcov on a cross-sectional design, which uses its own

# result class.

vcov_type: Optional[str] = field(default=None)

cluster_name: Optional[str] = field(default=None)

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)

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,

)

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/|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)

def print_summary(self, alpha: Optional[float] = None) -> None:

"""Print the summary to stdout."""

print(self.summary(alpha))

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,

}

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

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 ""

@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]

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)

@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' is rejected at fit-time for MultiPeriodDiD (Phase 1

# supports cross-sectional Conley only via direct compute_robust_vcov);

# see _format_vcov_label.

vcov_type: Optional[str] = field(default=None)

cluster_name: Optional[str] = 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

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)

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,

)

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/|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)

def print_summary(self, alpha: Optional[float] = None) -> None:

"""Print the summary to stdout."""

print(self.summary(alpha))

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]

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,

}

# 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

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)

@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

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})"

)

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)

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}")

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