charmlab / recourseLinks
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
☆36Updated 2 years ago
Alternatives and similar repositories for recourse
Users that are interested in recourse are comparing it to the libraries listed below
Sorting:
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆27Updated 3 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆77Updated 4 years ago
- Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"☆31Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆68Updated 8 months ago
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆84Updated last year
- Uncertainty in Conditional Average Treatment Effect Estimation☆33Updated 4 years ago
- References for Papers at the Intersection of Causality and Fairness☆18Updated 6 years ago
- A lightweight implementation of removal-based explanations for ML models.☆58Updated 4 years ago
- ☆18Updated last year
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆53Updated 4 years ago
- ☆32Updated 7 years ago
- ☆44Updated 3 years ago
- ☆22Updated last year
- Neural Additive Models (Google Research)☆71Updated 3 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆131Updated 4 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆85Updated last year
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆59Updated last year
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆66Updated 5 months ago
- ☆33Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- Code for "Causal autoregressive flows" - AISTATS, 2021☆45Updated 4 years ago
- An Empirical Study of Invariant Risk Minimization☆27Updated 5 years ago
- Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.☆29Updated last year
- Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", b…☆21Updated 2 years ago
- ☆40Updated 6 years ago
- Code for "Generative causal explanations of black-box classifiers"☆34Updated 4 years ago