A Primer on Causal Analysis

TL;DR

This paper offers a conceptual overview of causal analysis, focusing on discrete variables and causal effect estimation from observational data, and introduces four main schools of thought in the field.

Contribution

It provides a clear framework and categorization of causal analysis methods, enhancing understanding of different approaches and their applications.

Findings

Causal effect estimation is complex for continuous variables.

Four schools of thought structure causal analysis methodologies.

The paper clarifies conceptual challenges in causal inference.

Abstract

We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data. The presented approaches apply also to continuous variables, but the issue of estimation becomes more complex. We then introduce the four schools of thought for causal analysis