# `causalis.scenarios.iv`

## Examples

> > > from causalis.scenarios.iv import generate_offer_iv_26, IIVM

> > > data is returned as IVCausalData by default

> > > data = generate_offer_iv_26(n=1000) model = IIVM() model.fit(data) result = model.estimate()

Instrumental-variable (IV) scenario and estimators.

This module provides tools for causal inference when there is unobserved confounding between treatment and outcome, but a valid instrument is available.

The primary estimator is the :class:`IIVM` (Iterated Instrumental Variable Model), which uses double machine learning to estimate the Local Average Treatment Effect (LATE).

## Subpackages

- `causalis.scenarios.iv.refutation`
  - `causalis.scenarios.iv.refutation.diagnostics`
  - `causalis.scenarios.iv.refutation.iv_confounders_balance`

## Submodules

- `causalis.scenarios.iv.dgp`
- `causalis.scenarios.iv.model`

## Package Contents

### Data

`__all__` |

### API

causalis.scenarios.iv.__all__

  [‘IIVM’, ‘IVCausalEstimate’, ‘generate_offer_iv_26’]