Joeyonng / decision-rules-network
This repository contains the source code of the paper "Learning Accurate and Interpretable Decision Rule Sets from Neural Networks".
☆12Updated 3 years ago
Alternatives and similar repositories for decision-rules-network:
Users that are interested in decision-rules-network are comparing it to the libraries listed below
- Example causal datasets with consistent formatting and ground truth☆82Updated last year
- pyCausalFS:A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification☆69Updated 4 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆59Updated 4 years ago
- Causal discovery for time series☆96Updated 3 years ago
- CPDAG Estimation using PC-Algorithm☆95Updated 2 years ago
- Rule Extraction Methods for Interactive eXplainability☆43Updated 2 years ago
- Code for paper: NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge☆23Updated 2 years ago
- A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matr…☆115Updated last year
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.☆35Updated 2 years ago
- ☆92Updated 2 years ago
- Code for "NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning"☆43Updated 2 years ago
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆50Updated 6 years ago
- Makes algorithms/code in Tetrad available in Python via JPype☆73Updated this week
- Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Search…☆56Updated last month
- Causal Neural Nerwork☆102Updated last year
- Causal Discovery from Nonstationary/Heterogeneous Data.☆53Updated 4 years ago
- Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data☆209Updated 3 years ago
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 3 years ago
- Statistical Recurrent Unit based time series generative models for detecting nonlinear Granger causality☆34Updated 4 years ago
- Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning☆41Updated last month
- Implement PC algorithm in Python | PC 算法的 Python 实现☆116Updated last year
- Python code of Hilbert-Schmidt Independence Criterion☆87Updated 2 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆30Updated 5 years ago
- Continuous Industrial Process datasets for benchmarking Causal Discovery methods☆28Updated 2 years ago
- ☆204Updated 2 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆73Updated 3 years ago
- Method based on neural networks and variational inference for causal discovery under latent interventions, i. e. learning a shared causal…☆19Updated 3 years ago
- Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. …☆52Updated 4 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆99Updated 5 years ago
- A generalized score-based method for Causal Discovery☆16Updated 4 years ago