General

This repository provides code and examples for generating nearest counterfactual explanations and minimal consequential interventions. The following papers are supported:

• 2017.06 Feature Tweaking (4c691b4 @ https://github.com/upura/featureTweakPy)
• 2019.01 Actionable Recourse (9387e6c @ https://github.com/ustunb/actionable-recourse)
• 2019.07 Minimum Observable
• 2019.05 MACE
• 2020.02 MINT

Code Pre-requisites

First,

$ git clone https://github.com/amirhk/mace.git
$ pip install virtualenv
$ cd mace
$ virtualenv -p python3 _venv
$ source _venv/bin/activate
$ pip install -r pip_requirements.txt
$ pysmt-install --z3 --confirm-agreement

Then refer to

$ python batchTest.py  --help

and run as follows

$ python batchTest.py -d *dataset* -m *model* -n *norm* -a *approach* -b 0 -s *numSamples*

For instance, you may run

$ python batchTest.py -d adult -m lr -n zero_norm -a AR -b 0 -s 1
$ python batchTest.py -d credit -m mlp -n one_norm -a MACE_eps_1e-3 -b 0 -s 1
$ python batchTest.py -d german -m tree -n two_norm -a MINT__eps_1e-3 -b 0 -s 1
$ python batchTest.py -d mortgage -m forest -n infty_norm -a MINT__eps_1e-3 -b 0 -s 1

Finally, view the results under the _experiments folder.

Specific considerations for minimal interventions

For mortgage data, where a causal structure governs the world, AND all variables
are actionable and mutable, we should expect to see int_dist <= ? >= cfe_dist,
but cfe_dist <= scf_dist. You can assert this by running the following:

$ python batchTest.py -d mortgage -m lr -n one_norm -a MINT_eps_1e-5 MACE_eps_1e-5 -b 0 -s 10

Then you can compare the distances resulting fron MACE and MINT as outputted in the console. Do make sure to run batchTest.py with loadData.loadDataset(load_from_cache = True) so that MACE and MINT use the same data and the resulting comparison is fair.

Using git-hooks script for sanity checking

There is a pre-push script under _hooks/ which can be used to check MACE under different setups.
Specifically, it checks for successfully running of the code and the closeness of the generated CFEs
to the previously-saved (approximately) optimal ones. You can either manually call the script from MACE root directory by
_hooks/pre-push or place it under your local .git/hooks/ directory to run automatically before every push.
In this case, please remember to give it the required permissions:

$ chmod +x .git/hooks/pre-push