[Binder]
This repository contains a small Python package created for STAT159 final project. This small package will implement “Method 3” of Li and Ding paper in Python to find 2-sided 1−𝛼 confidence bounds for the average treatment effect in a randomized experiment with binary outcomes and two treatments (active treatment and control).
It has a single source directory (cabin) with an __init__.py file and one "implementation" file (cibin.py) as well as a few tests in cabin/tests.
A top-level notebook called cibin-demo.ipynb that reproduces, using our implementation, column “3” of table I in the Li and Ding paper.
There are also four analysis-*.ipynb files and one pdf file analysis-I-IV.pdf, containing analysis related to data from the Regeneron study. analysis-I.ipynb, analysis-II.ipynb , analysis-III.ipynb and analysis-1V.ipynb conducted analysis listed in the finial project requirments. analysis-I-IV.pdf complied all four part of analysis together into a same pdf file.
And utils.py, it include implementation of sterne method which is used in analysis-I for underlying hypergeometric confidence intervals.
In addition to this README.md it includes some basic infrastructure: LICENSE, requirements.txt, and setup.py files.
This project can currently only be installed from source, via
pip install .
or for a development installation via
pip install -e .
You can run the project test suite via
pytest cibin
This project is released under the terms of the BSD 3-clause License.