siplab-gt / generative-causal-explanationsLinks
Code for "Generative causal explanations of black-box classifiers"
☆35Updated 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
Sorting:
- ☆63Updated 5 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
- ☆44Updated 3 years ago
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆51Updated 4 years ago
- An Empirical Study of Invariant Risk Minimization☆27Updated 5 years ago
- Self-Explaining Neural Networks☆43Updated 5 years ago
- Self-Explaining Neural Networks☆13Updated 2 years ago
- References for Papers at the Intersection of Causality and Fairness☆18Updated 6 years ago
- Tensorflow implementation of Invariant Rationalization☆49Updated 2 years ago
- ☆31Updated 4 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- ☆65Updated last year
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆54Updated 3 years ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆64Updated 5 years ago
- ☆26Updated 5 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆91Updated 5 years ago
- Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)☆33Updated 4 years ago
- ☆50Updated 2 years ago
- Code for the NIPS 2018 paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"☆18Updated 4 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆32Updated 6 years ago
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago
- ☆32Updated 7 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆30Updated 3 years ago
- Disentangled gEnerative cAusal Representation (DEAR)☆62Updated 3 years ago
- Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)☆14Updated 3 years ago
- Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotligh…☆44Updated last year
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Repository for theory and methods for Out-of-Distribution (OoD) generalization☆63Updated 3 years ago