Documentation

What is Causal Analysis?

Causal analysis is a branch of statistics and data science that focuses on identifying cause-and-effect relationships between variables. Unlike traditional statistical methods that only identify correlations, causal analysis aims to determine whether changes in one variable directly cause changes in another.

Why is Causal Analysis Important?

Understanding causality is crucial for making informed decisions. While correlations can be useful for predictions, they can be misleading for interventions. For example, ice cream sales and drowning incidents are correlated (both increase in summer), but banning ice cream won't reduce drownings. Causal analysis helps distinguish between:

• Direct causal relationships
• Reverse causality
• Common causes (confounding variables)
• Spurious correlations

Key Concepts in Causal Analysis

Causal analysis relies on several key concepts:

• Counterfactuals: What would have happened if the cause had been different?
• Causal Graphs: Visual representations of causal relationships between variables
• Treatment Effects: Measuring the impact of an intervention
• Confounding: Variables that influence both the cause and effect