Causal learner: A toolbox for causal structure and Markov blanket learning
نویسندگان
چکیده
Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions generating simulated Bayesian network data, set of state-of-the-art global algorithms, local MB evaluating algorithms. The data generation part written in R, the rest MATLAB. aims to provide researchers practitioners with an open-source platform development evaluation new project available at http://bigdata.ahu.edu.cn/causal-learner.
منابع مشابه
Using Markov Blankets for Causal Structure Learning
We show how a generic feature-selection algorithm returning strongly relevant variables can be turned into a causal structure-learning algorithm. We prove this under the Faithfulness assumption for the data distribution. In a causal graph, the strongly relevant variables for a node X are its parents, children, and children’s parents (or spouses), also known as the Markov blanket of X . Identify...
متن کاملLearning Bayesian Network Structure using Markov Blanket in K2 Algorithm
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
متن کاملCausal imprinting in causal structure learning.
Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent g...
متن کاملLocal Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
We present an algorithmic framework for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large data sets with relatively small samples. The selected feature sets can be used for causal discovery and classification. The framework (Generalized Local Learning, or GLL) can be instantiated in numerous ways...
متن کاملLocal Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
In part I of this work we introduced and evaluated the Generalized Local Learning (GLL) framework for producing local causal and Markov blanket induction algorithms. In the present second part we analyze the behavior of GLL algorithms and provide extensions to the core methods. Specifically, we investigate the empirical convergence of GLL to the true local neighborhood as a function of sample s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2022
ISSN: ['1872-7344', '0167-8655']
DOI: https://doi.org/10.1016/j.patrec.2022.09.021