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

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

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

## Module Contents

### Functions

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

* - {py:obj}`generate_multitreatment_gamma_26 <causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_gamma_26>`
  - ```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_gamma_26
    :summary:
    ```
* - {py:obj}`generate_multitreatment_binary_26 <causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_binary_26>`
  - ```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_binary_26
    :summary:
    ```
* - {py:obj}`generate_multitreatment_irm_26 <causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_irm_26>`
  - ```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_irm_26
    :summary:
    ```
* - {py:obj}`generate_multi_dml_cx_26 <causalis.scenarios.multi_unconfoundedness.dgp.generate_multi_dml_cx_26>`
  - ```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multi_dml_cx_26
    :summary:
    ```
````

### Data

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

* - {py:obj}`multi_dml_cx_26 <causalis.scenarios.multi_unconfoundedness.dgp.multi_dml_cx_26>`
  - ```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.multi_dml_cx_26
    :summary:
    ```
````

### API

````{py:function} generate_multitreatment_gamma_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) -> typing.Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]
:canonical: causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_gamma_26

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_gamma_26
```
````

````{py:function} generate_multitreatment_binary_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) -> typing.Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]
:canonical: causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_binary_26

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_binary_26
```
````

````{py:function} generate_multitreatment_irm_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) -> typing.Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]
:canonical: causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_irm_26

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_irm_26
```
````

````{py:function} generate_multi_dml_cx_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) -> typing.Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]
:canonical: causalis.scenarios.multi_unconfoundedness.dgp.generate_multi_dml_cx_26

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.generate_multi_dml_cx_26
```
````

````{py:data} multi_dml_cx_26
:canonical: causalis.scenarios.multi_unconfoundedness.dgp.multi_dml_cx_26
:value: >
   None

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.dgp.multi_dml_cx_26
```

````
