siplab-gt / generative-causal-explanations
Code for "Generative causal explanations of black-box classifiers"
☆33Updated 4 years ago
Alternatives and similar repositories for generative-causal-explanations:
Users that are interested in generative-causal-explanations are comparing it to the libraries listed below
- ☆64Updated 4 years ago
- Self-Explaining Neural Networks☆13Updated last year
- ☆65Updated 7 months ago
- ☆44Updated 3 years ago
- Tensorflow implementation of Invariant Rationalization☆49Updated 2 years ago
- ☆30Updated 3 years ago
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆50Updated 3 years ago
- Self-Explaining Neural Networks☆40Updated 5 years ago
- Code for the NIPS 2018 paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"☆18Updated 3 years ago
- ☆32Updated 6 years ago
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.☆35Updated 2 years ago
- Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)☆32Updated 3 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- Code for "Neural causal learning from unknown interventions"☆99Updated 4 years ago
- This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help …☆24Updated 2 years ago
- An Empirical Study of Invariant Risk Minimization☆27Updated 4 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆30Updated 3 years ago
- Repository for theory and methods for Out-of-Distribution (OoD) generalization☆63Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆30Updated 5 years ago
- ☆27Updated 4 years ago
- Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"☆18Updated 3 years ago
- Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style☆51Updated 3 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Code for "Causal autoregressive flows" - AISTATS, 2021☆44Updated 3 years ago
- ☆50Updated last year
- Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)☆12Updated 3 years ago
- Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"☆30Updated last year