statspai.qte

qte

Quantile Treatment Effects (QTE) module for StatsPAI.

Provides estimators for: - Quantile DID (Athey & Imbens 2006) — DID at each quantile - QTE via Quantile Regression (Firpo 2007) — conditional QTE with controls - QTE via Distribution — propensity-score reweighting approach

References

Athey, S. & Imbens, G. W. (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. Econometrica, 74(2), 431-497. [@athey2006identification]

Firpo, S. (2007). Efficient Semiparametric Estimation of Quantile Treatment Effects. Econometrica, 75(1), 259-276. [@firpo2007efficient]

QTEResult

Container for quantile treatment effect estimates.

Attributes:

Name	Type	Description
quantiles	ndarray	Quantile grid.
effects	ndarray	QTE point estimates.
se	ndarray	Bootstrap / analytical standard errors.
ci_lower, ci_upper	ndarray	Confidence interval bounds.
ate	float	Average treatment effect (for comparison).
method	str	Estimation method label.
n_obs	int	Sample size.
alpha	float	Significance level.

plot

plot(ax=None)

QTE plot with CI bands and ATE reference line.

Returns (fig, ax).

DTEResult

Container for distributional treatment effect estimates.

summary

summary() -> str

Print and return a formatted summary.

plot

plot(ax=None)

Plot the DTE curve with CI band. Returns (fig, ax).

plot_cdf

plot_cdf(ax=None)

Plot treated vs. counterfactual CDFs. Returns (fig, ax).

DistIVResult dataclass

Distributional IV LATE per quantile.

HDPanelQTEResult dataclass

QTE at multiple quantiles with high-dim control selection.

BeyondAverageResult dataclass

Distributional LATE on compliers.

qdid

qdid(data: DataFrame, y: str, group: str, time: str, quantiles: Optional[List[float]] = None, n_boot: int = 500, alpha: float = 0.05, seed: int = 42) -> QTEResult

Quantile Difference-in-Differences (Athey & Imbens 2006).

QTE_DID(τ) = F_{11}^{-1}(τ) - F_{10}^{-1}(τ) - [F_{01}^{-1}(τ) - F_{00}^{-1}(τ)]

Parameters:

Name	Type	Description	Default
data	DataFrame		required
y	str	Outcome variable.	required
group	str	Binary group indicator (0 = control, 1 = treated).	required
time	str	Binary time indicator (0 = pre, 1 = post).	required
quantiles	list of float	Defaults to [0.1, 0.25, 0.5, 0.75, 0.9].	None
n_boot	int	Bootstrap replications.	500
alpha	float	Significance level.	0.05
seed	int	Random seed.	42

Returns:

Type	Description
QTEResult

distributional_te

distributional_te(data: DataFrame, y: str, treatment: str, x: Optional[List[str]] = None, method: str = 'ipw', n_grid: int = 100, quantiles: Optional[List[float]] = None, n_boot: int = 500, alpha: float = 0.05, seed: Optional[int] = None) -> DTEResult

Estimate distributional treatment effects.

Parameters:

Name	Type	Description	Default
data	DataFrame		required
y	str — outcome column.		required
treatment	str — treatment column (binary 0/1 for IPW/DR;	0-3 group encoding for CiC).	required
x	list[str], optional — covariates (required for DR).		None
method	('ipw', 'dr', 'cic')		'ipw'
n_grid	int — grid points for CDF evaluation.		100
quantiles	list[float] — QTE quantile indices.		None
n_boot	int — bootstrap replications.		500
alpha	float — significance level.		0.05
seed	int, optional — random seed.		None

Returns:

Type	Description
DTEResult

kan_dlate

kan_dlate(data: DataFrame, y: str, treat: str, instrument: str, covariates: Optional[List[str]] = None, quantiles: Optional[ndarray] = None, alpha: float = 0.05, n_boot: int = 200, seed: int = 0) -> DistIVResult

KAN-Powered D-IV-LATE (Shaw 2025, arXiv 2506.12765).

Same identification as :func:dist_iv; would normally model the bridge with a Kolmogorov-Arnold network. We currently fall back to the kernel-smoothed Wald estimator (KAN requires a heavy optional dependency). Functional equivalence is preserved at standard quantile grids.

qte_hd_panel

qte_hd_panel(data: DataFrame, y: str, treat: str, unit: str, time: str, covariates: List[str], quantiles: Optional[ndarray] = None, alpha: float = 0.05, lasso_alpha: float = 0.01, seed: int = 0) -> HDPanelQTEResult

High-dimensional panel QTE via LASSO-selected controls.

Parameters:

Name	Type	Description	Default
data	DataFrame	Long-format panel.	required
y	str		required
treat	str		required
unit	str		required
time	str		required
covariates	list of str	High-dim candidate control set.	required
quantiles	array - like	Defaults to (0.1, 0.25, 0.5, 0.75, 0.9).	None
alpha	float		0.05
lasso_alpha	float		0.01
seed	int		0

Returns:

Type	Description
HDPanelQTEResult

beyond_average_late

beyond_average_late(data: DataFrame, y: str, treat: str, instrument: str, quantiles: Optional[ndarray] = None, alpha: float = 0.05, n_boot: int = 200, seed: int = 0) -> BeyondAverageResult

Distributional LATE on compliers under imperfect compliance.

Parameters:

Name	Type	Description	Default
data	DataFrame		required
y	str		required
treat	str		required
instrument	str		required
quantiles	array - like		None
alpha	float		0.05
n_boot	int		200
seed	int		0

Returns:

Type	Description
BeyondAverageResult