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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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 of the Bayesian network

To implement the NOTEARS algorithm to learn the structure of the Bayesian network with your data, we will follow these steps:

  1. Use the simulated data to learn the structure using from_pandas, which applies the NOTEARS algorithm.

  2. Create a Bayesian network from the learned structure.

  3. Finally, we can visualize the learned structure.

Let's proceed with these steps:

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