Visualizing Causality Using Causal Graphs

Guide to Causal Graphs and Their Role in Causal Inference

Previous articles explored The Traps of Blindly Trusting Data: Simpson’s Paradox and An intuitive introduction to causal inference.

This blog dives into causal graphs, why we need them, graph terminology, Causal graph assumptions, and steps to draw a Causal graph.

Recap of What is Causal Inference

Causal inference is the process of understanding cause-and-effect relationships, focusing on inferring the causal effect of an intervention, treatment, or exposure on a particular outcome.

Association refers to a statistical relationship between two variables. When two variables are associated, changes in one variable tend to coincide with changes in the other variable, but it’s uncertain if one directly causes the other.

Association alone does not imply causation.

Causation implies a direct cause-and-effect relationship between two variables.

A causal relationship exists when changes in one variable directly lead to changes in another variable.

Bias plays a key role in distinguishing between association and causation in the context of causal inference.

What is a Causal Graph?

A Causal graph is a graphical representation used in causal…