Designing Graphical Causal Bayesian Networks in Python/

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Algorithm to Learn the Structure of the BN

Learn how to build the structure of the BN using an algorithm and how to manually correct the structure if needed.

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Using NOTEARS to create the structure of the Bayesian network
Post learning adjustments

Python library CausalNex supports the NOTEARS (Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for Structure learning) algorithm for structure learning in Bayesian networks. NOTEARS is an algorithm designed to learn the structure of a directed acyclic graph (DAG) from data without resorting to combinatorial candidate generation and selection, which is common in other structure learning approaches.

The use of NOTEARS in CausalNex allows for more efficient and scalable structure learning in large datasets where traditional methods might be impractical due to their computational complexity. By leveraging gradient-based optimization techniques, NOTEARS can efficiently find structures that are likely to explain the causal relationships in the data, making it a valuable tool for causal analysis in various fields, including economics, epidemiology, and machine learning research.

Using NOTEARS to create the structure

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Introduction to Graphs

Exploring Graphs Characteristics in Python

Bayesian Networks

Graph Patterns in Bayesian Networks

Structure-Based Hyperparameters in Bayesian Network

Building a Graph-Based Bayesian Network

Data Based Bayesian Networks

Building a Complex Bayesian Network

Create a Knowledge and Data-Based Bayesian Network

Evaluating the Output and Performance

Conclusion

Algorithm to Learn the Structure of the BN

Using NOTEARS to create the structure