# CATEResult


Conditional average treatment effect estimates.


Usage

``` python
CATEResult(
    x,
    cate,
    se,
    lower,
    upper,
    variable,
    level,
)
```


Returned by [CausalGAM.cate()](CausalGAM.md#whittaker.CausalGAM.cate) when the model was fit with `method="interactive"`. Represents the treatment effect as a smooth function of one confounder variable, evaluated on a grid (or on user-supplied covariate data), together with pointwise confidence bands.


## Attributes


`x: NDArray`  
Covariate values of `variable` at which CATE is evaluated.

`cate: NDArray`  
CATE estimates, `tau(x) = E[Y(1) - Y(0) | X = x]`, at each value of `x`.

`se: NDArray`  
Standard errors of the CATE estimates.

`lower: NDArray`  
Lower pointwise confidence bounds, `cate - z * se`.

`upper: NDArray`  
Upper pointwise confidence bounds, `cate + z * se`.

`variable: str`  
Name of the conditioning (confounder) variable that CATE is plotted against.

`level: float`  
Confidence level used for the bands.