In this tutorial, we will be exploring a Causal fairness analysis, based on a real-world dataset. Out theoretical framework would be Structural Theory of Causation and we will analyse based on existing work "Causal Fairness Analysis" by Plecko and Bareinboim (2022), and our own additional inferences.

For test data, we will use ProPoblica COMPAS dataset, along with some other datasets. The eventual goal is to transform the idea for our own problem domain and dataset.

We will generate a computational pipeline, present it theoretically and show the data analysis in parallel. The goal is to start a tutorial and move towards a paper and eventually a grant.

All documents in ordered list:

1. High level overview
2. Primary planning
3. Dataset specific Exploration
4. Main code notebook
5. NCATS bias detection challenge details

In parallel, we planned to handle Bias Detection Tools in Health Care Challenge from NCATS, since the problem statement aligned a lot in between these two projects. Here is the relevant writeup for that.