Here's the latest stable version.
- DiCE now supports Pytorch, Tensorflow 1.x and 2.
- Includes a Variational AutoEncoder-based method to generate counterfactual examples, based on https://arxiv.org/abs/1912.03277. This method is much faster--try it out!
- Support for private data, when only aggregate training data statistics are available to generate counterfactuals
- Updated and faster post-hoc sparsity enhancer module for counterfactuals
- Includes bug fixes for DiCE and tests for most DiCE functionalities.
- More notebooks and detailed docs at http://interpret.ml/DiCE/
Big thanks to @raam93 for leading the updates, and to @divyat09 for adding the VAE method.