# Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning

arXiv: https://arxiv.org/abs/2103.06544 · HTML: https://mlsanity.com/paper/2103.06544/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Causality-based Feature Selection: Methods and Evaluations](https://mlsanity.com/paper/1911.07147/) (2019)

## Built on by (most cited first, 5 of 5)

- [Local Causal Discovery for Estimating Causal Effects](https://mlsanity.com/paper/2302.08070/) (2023)
- [Fair Causal Feature Selection](https://mlsanity.com/paper/2306.10336/) (2023)
- [Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference](https://mlsanity.com/paper/2602.00775/) (2026)
- [Learning linear acyclic causal model including Gaussian noise using ancestral relationships](https://mlsanity.com/paper/2409.00417/) (2024)
- [Hybrid Local Causal Discovery](https://mlsanity.com/paper/2412.19507/) (2024)

## Abstract (arXiv)

Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of-the-art local causal structure learning algorithms, a set of state-of-the-art MB learning algorithms, and functions for evaluating algorithms. The data generation part of Causal Learner is written in R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to provide researchers and practitioners with an open-source platform for causal learning from data and for the development and evaluation of new causal learning algorithms. The Causal Learner project is available at http://bigdata.ahu.edu.cn/causal-learner.