Instrumental Variables Regression in Python

ivmodels implements

• K-Class estimators, including the Limited Information Maximum Likelihood (LIML) and the Two-Stage Least Squares (TSLS) estimator.
• Tests and confidence sets for the parameters of the model, including the Anderson-Rubin test, the Lagrange multiplier test, the (conditional) likelihood-ratio test, and the Wald test.
• Auxiliary tests such as Anderson's (1951) test of reduced rank (a multivariate extension to the first-stage F-test), the J-test (including its LIML variant), and Scheidegger et al.'s residual prediction test of well-specification.

See the docs and the examples therein for more details. See this document for an introduction to the estimators, tests, and their properties.

If you use this code, consider citing

@article{londschien2025statistician,
  title={A statistician's guide to weak-instrument-robust inference in instrumental variables regression with illustrations in {Python}},
  author={Londschien, Malte},
  journal={arXiv preprint arXiv:2508.12474},
  year={2025}
}

and

@article{londschien2024weak,
  title={Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets},
  author={Londschien, Malte and B{\"u}hlmann, Peter},
  journal={arXiv preprint arXiv:2407.15256},
  year={2024}
}

Installation

You can install ivmodels from conda (recommended):

conda install -c conda-forge ivmodels

or pip:

pip install ivmodels