Statistics Software

A software package supporting do-calculus and other causal inference tools.


May 2020 - April 2022



This project began under the supervision of Dr. Neufeld in May 2020 while I was a Summer Research Assistant at the University of Saskatchewan. This role ran from May 2020 to August 2020, though I have continued as a Student Research Assistant since. I have enjoyed this opportunity to work with Dr. Neufeld on this project (and a few other things!) and learned much in the process.

Scope / Scale

This project implements the entire do-calculus of Judea Pearl et. al, as well as the identification algorithm presented in Shpitser and Pearl, 2006. This work takes a graphical approach to enable the measurement of interventional distributions from observational data. This category of work is essentially a heavily-modified Bayesian network, and so this project is also a fully-functional Bayesian net, enabling standard probability queries as well.


The project is built entirely in Python 3, and is hosted on GitHub. An API is implemented, and the package can be installed through PyPI.