juangamella / causal-chamber-paper
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
☆12Updated 2 weeks ago
Related projects ⓘ
Alternatives and complementary repositories for causal-chamber-paper
- Dataset repository for the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jo…☆24Updated 2 weeks ago
- Dynamic causal Bayesian optimisation☆34Updated last year
- ☆23Updated 6 months ago
- A framework and specification language for simulating data based on graphical models☆18Updated 2 months ago
- A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.☆64Updated this week
- Cyclic Causal Inference☆12Updated 10 months ago
- TIme series DiscoverY BENCHmark (tidybench)☆37Updated 9 months ago
- GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference☆79Updated 11 months ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆38Updated 3 months ago
- Uses several statistical tests / algorithms on marginal / conditional distributions☆8Updated last year
- Literature and light wrappers for gaussian process models.☆45Updated 3 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆21Updated last year
- Bayesian optimization of discrete sequences☆19Updated 2 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆53Updated 3 years ago
- [Experimental] Global causal discovery algorithms☆89Updated this week
- Active Bayesian Causal Inference (Neurips'22)☆51Updated 3 months ago
- ☆11Updated 2 years ago
- Causal discovery with typed directed acyclic graphs (t-DAG). This is a ServiceNow Research project that was started at Element AI.☆13Updated last year
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆68Updated 3 years ago
- PyBNesian is a Python package that implements Bayesian networks.☆41Updated 2 months ago
- Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.☆24Updated 2 weeks ago
- This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumer…☆28Updated this week
- ☆19Updated 7 months ago
- Causal Modeling with Stationary Diffusions, AISTATS 2024☆16Updated 4 months ago
- Bayesian optimization with conformal coverage guarantees☆27Updated 2 years ago
- Contains the code to run the different models considered in the paper "Valid prediction intervals for regression problems"☆19Updated 2 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆80Updated 7 months ago
- Distributional Gradient Boosting Machines☆26Updated last year
- Python package for (conditional) independence testing and statistical functions related to causality.☆22Updated last month
- Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…☆18Updated 3 years ago