juangamella / aicpLinks
Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", by Juan L Gamella and Christina Heinze-Deml.
☆21Updated 2 years ago
Alternatives and similar repositories for aicp
Users that are interested in aicp are comparing it to the libraries listed below
Sorting:
- Diffusion Models for Causal Discovery☆88Updated 2 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆23Updated 2 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆86Updated last year
- Experiments to reproduce results in Interventional Causal Representation Learning.☆26Updated 2 years ago
- Code repository of the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" and "iCITRIS: Causal Representation Lear…☆56Updated 2 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆87Updated last year
- ☆32Updated 7 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 4 years ago
- DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks☆58Updated 2 months ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 2 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆37Updated 2 years ago
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆68Updated 9 months ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆36Updated last year
- Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022☆45Updated 3 years ago
- Code for "Neural causal learning from unknown interventions"☆105Updated 5 years ago
- Official Implementation of the paper "Variational Causal Networks: Approximate Bayesian Inference over Causal Structures"☆17Updated 4 years ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆33Updated 2 years ago
- Dynamic causal Bayesian optimisation☆40Updated 2 years ago
- ☆25Updated last year
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆35Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆28Updated 4 years ago
- Code for ICE-BeeM paper - NeurIPS 2020☆87Updated 4 years ago
- ☆22Updated 2 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆70Updated last year
- Quantile risk minimization☆24Updated last year
- Code for "Causal autoregressive flows" - AISTATS, 2021☆45Updated 4 years ago
- Variational Auto-Regressive Gaussian Processes for Continual Learning☆23Updated 4 years ago