Skip to content

Implementation of "Causal Generative Explainers using Counterfactual Inference".

Notifications You must be signed in to change notification settings

wtaylor17/CDGMExplainers

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

25 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CGMExplainers

Implementation of "Causal Generative Explainers using Counterfactual Inference".

The Morpho-MNIST data used in the paper can be downloaded here.

Models can be downloaded here. Once downloaded, this is what you should pass as the --model-dir argument to many scripts.

Precomputed shap values can be downloaded here for the --shap-value-dir argument to certain scripts.

Precomputed metrics for IM1/IM2/oracle scores can be downloaded here. These are passed as the --metrics-csv argument to appropriate scripts.

If any of the above links do not work, please reach out to me at wl647481@dal.ca

Notes on --data-dir

Sometimes it expects the directory of precomputed CFs rather than the directory with the whole dataset. The scripts where this is the case are image_shap_evolution.py, mnist_cf_comparisons.py, and contrastive_evolution.py.

About

Implementation of "Causal Generative Explainers using Counterfactual Inference".

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages