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

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

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

## Module Contents

### Classes

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

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

### API

`````{py:class} MultiTreatmentIRM(data: typing.Optional[causalis.data_contracts.multicausaldata.MultiCausalData] = None, ml_g: typing.Any = None, ml_m: typing.Any = None, *, n_folds: int = 5, n_rep: int = 1, normalize_ipw: bool = False, trimming_rule: str = 'truncate', trimming_threshold: float = 0.01, random_state: typing.Optional[int] = None, n_jobs: int = 1, store_diagnostics: bool = True)
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM

Bases: {py:obj}`sklearn.base.BaseEstimator`

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM
```

```{rubric} Initialization
```

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.__init__
```

````{py:method} fit(data: typing.Optional[causalis.data_contracts.multicausaldata.MultiCausalData] = None, *, store_diagnostics: typing.Optional[bool] = None) -> causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.fit

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.fit
```

````

````{py:method} estimate(score: str = 'ATE', alpha: float = 0.05, diagnostic_data: bool = True) -> causalis.data_contracts.multicausal_estimate.MultiCausalEstimate
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.estimate

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.estimate
```

````

````{py:property} diagnostics_
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.diagnostics_
:type: typing.Dict[str, typing.Any]

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.diagnostics_
```

````

````{py:property} coef
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.coef
:type: numpy.ndarray

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.coef
```

````

````{py:property} se
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.se
:type: numpy.ndarray

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.se
```

````

````{py:property} pvalues
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.pvalues
:type: numpy.ndarray

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.pvalues
```

````

````{py:property} summary
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.summary
:type: pandas.DataFrame

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.summary
```

````

````{py:property} orth_signal
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.orth_signal
:type: numpy.ndarray

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.orth_signal
```

````

````{py:method} sensitivity_analysis(cf_y: typing.Optional[float] = None, r2_d: typing.Any = 0.0, rho: typing.Any = 1.0, H0: float = 0.0, alpha: float = 0.05, *, r2_y: typing.Optional[float] = None) -> causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.sensitivity_analysis

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.sensitivity_analysis
```

````

````{py:method} confint() -> pandas.DataFrame
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.confint

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.confint
```

````

````{py:method} __repr__() -> str
:canonical: causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.__repr__

```{autodoc2-docstring} causalis.scenarios.multi_unconfoundedness.model.MultiTreatmentIRM.__repr__
```

````

`````
