Counterfactual Fairness (Kusner et al. 2017) Python Replication

Allows for replication of the Counterfactual Fairness paper results in Python using PyStan.

Options are:

• -do_l2: Performs the replication of the L2 (Fair K) model, which can take a while depending on computing power
• -save_l2: Saves the resultant models (or not) for the L2 (Fair K) model, which produces large-ish files (100s MBs)

Dependencies include:

• Python 3.5.5
• NumPy 1.14.3
• Pandas 0.23.0
• Scikit-learn 0.19.1
• PyStan 2.17.1.0
• StatsModels 0.9.0

To run with default settings (Perform L2 tests, but don't save the Posterior Samples), type:

python CounterFair_Emulate.py

The following results should print:

Unfair RMSE:                0.870
FTU RMSE:                   0.891
Level 2 (Fair K) RMSE:      0.929
Level 3 (Fair Add) RMSE:    0.918