TreatmentEffect

Average treatment effect estimate with inference.

Usage

Source

TreatmentEffect(
    ate,
    se,
    ci_lower,
    ci_upper,
    level,
    p_value,
    method,
    n_obs,
)

Returned by CausalGAM.treatment_effect(), this holds the debiased/double-machine-learning estimate of the average treatment effect (ATE) together with its standard error, confidence interval, and a Wald test against the null of no effect.

Attributes

ate: float

Estimated average treatment effect: the coefficient theta in the partially linear model Y = theta * D + f(X) + eps, or its interactive-model analogue.

se: float

Standard error of the ATE estimate, computed from the influence function of the DML moment condition.

ci_lower: float

Lower bound of the level-confidence interval, ate - z * se.

ci_upper: float

Upper bound of the level-confidence interval, ate + z * se.

level: float

Confidence level used to construct the interval (e.g. 0.95).

p_value: float

Two-sided p-value for H0: ATE = 0, from a normal (Wald) approximation.

method: str

Estimation method used ("partially_linear" or "interactive").

n_obs: int

Number of observations used in estimation.