dualbounds 2.0.0

Dual bounds for model-agnostic inference

A python implementation of the dual bounds framework for inference on partially identified estimands (and the solutions to optimization problems more generally). See https://dualbounds.readthedocs.io/en/latest/ for detailed documentation and tutorials.

Installation

To install dualbounds, just use pip:

pip install dualbounds

Documentation

Documentation and tutorials are available at https://dualbounds.readthedocs.io/en/latest/.

Quickstart

Given a response vector y, binary treatment vector W, covariate matrix X, and an (optional) propensity score vector pis, dualbounds allows analysts to perform inference on partially identified estimands of the form E[f(Y(0), Y(1), X)], for any choice of f. For example, the code below shows how to perform inference on P(Y(0) < Y(1)), the proportion of individuals who benefit from the treatment. Dual bounds can wrap on top of any machine learning model to provide provably valid confidence intervals in randomized experiments.

	import dualbounds as db
	from dualbounds.generic import DualBounds

	# Generate synthetic data
	data = db.gen_data.gen_regression_data(n=900, p=30, sample_seed=123)

	# Initialize dual bounds object
	dbnd = DualBounds(
	    f=lambda y0, y1, x: y0 < y1,
	    covariates=data['X'],
	    treatment=data['W'],
	    outcome=data['y'],
	    propensities=data['pis'],
	    outcome_model='ridge',
	)

	# Compute dual bounds and observe output
	results = dbnd.fit(alpha=0.05).results()

Reference

If you use dualbounds in an academic publication, please consider citing Ji, Lei, and Spector (2023). The bibtex is below:

@misc{ji2023modelagnostic,
      title={Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects}, 
      author={Wenlong Ji and Lihua Lei and Asher Spector},
      year={2023},
      eprint={2310.08115},
      archivePrefix={arXiv},
      primaryClass={econ.EM}
}