# {py:mod}`causalis.scenarios.cuped.dgp`

```{py:module} causalis.scenarios.cuped.dgp
```

```{autodoc2-docstring} causalis.scenarios.cuped.dgp
:allowtitles:
```

## Module Contents

### Functions

````{list-table}
:class: autosummary longtable
:align: left

* - {py:obj}`generate_cuped_tweedie_26 <causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26>`
  - ```{autodoc2-docstring} causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26
    :summary:
    ```
* - {py:obj}`make_cuped_binary_26 <causalis.scenarios.cuped.dgp.make_cuped_binary_26>`
  - ```{autodoc2-docstring} causalis.scenarios.cuped.dgp.make_cuped_binary_26
    :summary:
    ```
````

### API

````{py:function} generate_cuped_tweedie_26(n: int = 20000, seed: int = 42, add_pre: bool = True, pre_name: str = 'y_pre', pre_name_2: typing.Optional[str] = None, pre_target_corr: float = 0.82, pre_target_corr_2: typing.Optional[float] = None, pre_spec: typing.Optional[causalis.dgp.causaldata.preperiod.PreCorrSpec] = None, include_oracle: bool = False, return_causal_data: bool = True, theta_log: float = 0.38) -> typing.Union[pandas.DataFrame, causalis.dgp.causaldata.CausalData]
:canonical: causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26

```{autodoc2-docstring} causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26
```
````

````{py:function} make_cuped_binary_26(n: int = 10000, seed: int = 42, add_pre: bool = True, pre_name: str = 'y_pre', pre_target_corr: float = 0.65, pre_spec: typing.Optional[causalis.dgp.causaldata.preperiod.PreCorrSpec] = None, include_oracle: bool = True, return_causal_data: bool = True, theta_logit: float = 0.38) -> typing.Union[pandas.DataFrame, causalis.dgp.causaldata.CausalData]
:canonical: causalis.scenarios.cuped.dgp.make_cuped_binary_26

```{autodoc2-docstring} causalis.scenarios.cuped.dgp.make_cuped_binary_26
```
````
