Hello, and welcome to The Confounder! This is my very first blog post.
Why "The Confounder"?
You might be wondering about the name of my blog. In the realm of causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Understanding and accounting for confounders is crucial in drawing accurate conclusions from data. Just as confounders can muddy the waters of causal relationships, we aim to be the entity that confounds expectations, challenges assumptions, and brings clarity to complex topics.
Judea Pearl
No discussion about modern causal inference would be complete without acknowledging the monumental contributions of Judea Pearl. A computer scientist and philosopher, Pearl's work has revolutionized our understanding of causality in statistics and artificial intelligence.
Pearl's development of the do-calculus and causal diagrams has provided us with powerful tools to reason about cause and effect relationships. His framework has applications ranging from epidemiology to economics, from artificial intelligence to public policy.