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’]