syanga / pycit
(Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information estimators. Supports continuous, discrete, and mixed data, as well as multiprocessing.
☆28Updated 4 years ago
Alternatives and similar repositories for pycit:
Users that are interested in pycit are comparing it to the libraries listed below
- pyCausalFS:A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification☆67Updated 4 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆59Updated 4 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (Python version)☆20Updated 4 years ago
- CPDAG Estimation using PC-Algorithm☆95Updated 2 years ago
- Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning☆42Updated last week
- This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop a…☆59Updated 6 months ago
- Causal discovery for time series☆93Updated 3 years ago
- [Experimental] Global causal discovery algorithms☆98Updated 3 weeks ago
- Makes algorithms/code in Tetrad available in Python via JPype☆71Updated this week
- Example causal datasets with consistent formatting and ground truth☆78Updated last year
- Causal Discovery from Nonstationary/Heterogeneous Data.☆52Updated 4 years ago
- Fast and incremental explanations for online machine learning models. Works best with the river framework.☆54Updated 2 months ago
- This python code estimates conditional mutual information (CMI) and mutual information (MI) for discrete and/or continuous variables usin…☆35Updated 3 years ago
- Python code for NeurIPS 2018 paper "Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models"☆22Updated 5 years ago
- ☆92Updated last year
- Granger causality discovery for neural networks.☆209Updated 3 years ago
- Critical difference diagram with Wilcoxon-Holm post-hoc analysis.☆272Updated 2 years ago
- Code for the paper "Estimating Transfer Entropy via Copula Entropy"☆40Updated last year
- Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Search…☆55Updated last year
- A collection of algorithms of counterfactual explanations.☆50Updated 3 years ago
- A generalized score-based method for Causal Discovery☆16Updated 4 years ago
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆49Updated 5 years ago
- An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.☆68Updated last year
- For calculating Shapley values via linear regression.☆67Updated 3 years ago
- ☆59Updated 4 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
- Statistical Recurrent Unit based time series generative models for detecting nonlinear Granger causality☆31Updated 4 years ago
- Datasets for Causal-Structure-Learning Repo☆15Updated 4 years ago
- ☆204Updated last year
- G Square Conditional Independence Test☆11Updated 7 years ago