rpatrik96 / nl-causal-representationsLinks
This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).
☆18Updated 10 months ago
Alternatives and similar repositories for nl-causal-representations
Users that are interested in nl-causal-representations are comparing it to the libraries listed below
Sorting:
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆28Updated 4 years ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 2 years ago
- Uncertainty in Conditional Average Treatment Effect Estimation☆33Updated 4 years ago
- Random feature latent variable models in Python☆22Updated last year
- ☆31Updated 2 years ago
- Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"☆38Updated 2 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆84Updated last year
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆22Updated 2 years ago
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆66Updated 5 months ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated 2 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…☆18Updated 4 years ago
- Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", b…☆20Updated 2 years ago
- ☆11Updated 2 years ago
- Parametric and non-parametric conditional independence testing.☆10Updated 4 years ago
- Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.☆50Updated 5 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆85Updated last year
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆52Updated 4 years ago
- Official code repository to the corresponding paper.☆29Updated last year
- Graph matching and clustering by comparing heat kernels via optimal transport.☆26Updated 2 years ago
- ☆10Updated 3 years ago
- ☆15Updated last year
- Dynamic causal Bayesian optimisation☆39Updated 2 years ago
- ☆16Updated 3 years ago
- ☆51Updated 11 months ago
- [Python] Comparison of empirical probability distributions. Integral probability metrics (e.g. Kantorovich metric). f-divergences (e.g. K…☆11Updated 2 years ago
- ☆25Updated last year
- Code for a variety of nonlinear conditional independence tests and 'nonlinear Invariant Causal Prediction' to estimate the causal parents…☆17Updated 5 years ago
- DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks☆55Updated last year