Causal Chain Graphs in Python

Introduction

In this lesson, we will focus on unraveling the intricacies of the causal chain pattern, an essential graph structure in BN, as well as its practical implications in various real-world scenarios.

Causal chains represent sequences of relationships where one variable directly influences the next, creating a chain of cause-and-effect connections. Understanding and modeling these causal chains is pivotal for accurately capturing the dependencies among variables and making informed decisions based on data.

A causal-chain graph is a graphical representation of the causal relationships between variables in a system. It's a type of directed acyclic graph (DAG), where nodes represent variables, and directed edges (arrows) represent causal relationships between them. The graph is called a causal-chain graph because it captures the chains of cause-and-effect relationships among the variables.

For example, suppose we have a graph with variables A, B, and C. A is the cause of B and B is the cause of C. In this scenario, the common cause graph would be structured as follows: