# TreatmentEffect


Average treatment effect estimate with inference.


Usage

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


Returned by [CausalGAM.treatment_effect()](CausalGAM.md#whittaker.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.