Causal Inference: A Tale of Three Frameworks

TL;DR

This paper compares three major frameworks for causal inference—potential outcomes, structural equation models, and directed acyclic graphs—highlighting their connections, differences, and practical complementarities for researchers.

Contribution

It provides a comprehensive comparative introduction, clarifying the frameworks' relationships, strengths, and limitations for applied causal analysis.

Findings

The frameworks often yield compatible insights.

They have distinct assumptions and philosophical orientations.

Combined use enhances causal analysis in practice.

Abstract

Causal inference is a central goal across many scientific disciplines. Over the past several decades, three major frameworks have emerged to formalize causal questions and guide their analysis: the potential outcomes framework, structural equation models, and directed acyclic graphs. Although these frameworks differ in language, assumptions, and philosophical orientation, they often lead to compatible or complementary insights. This paper provides a comparative introduction to the three frameworks, clarifying their connections, highlighting their distinct strengths and limitations, and illustrating how they can be used together in practice. The discussion is aimed at researchers and graduate students with some background in statistics or causal inference who are seeking a conceptual foundation for applying causal methods across a range of substantive domains.