A Survey of Causal Inference Frameworks

TL;DR

This survey reviews the evolution and integration of major causal inference frameworks, including potential outcomes and graphical models, highlighting recent advances and unification efforts across disciplines.

Contribution

It provides a comprehensive overview of causal inference frameworks and discusses recent efforts to unify different approaches for broader applicability.

Findings

Summarizes key causal inference frameworks and their principles.

Highlights recent unification efforts of different causal models.

Aims to facilitate cross-domain understanding of causal inference.

Abstract

Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, it measures effects of treatments in observational data based on experimental designs and rigorous statistical inference to draw causal statements. One of the most influential framework in quantifying causal effects is the potential outcomes framework. On the other hand, causal graphical models utilizes directed edges to represent causalities and encodes conditional independence relationships among variables in the graphs. A series of research has been done both in reading-off conditional independencies from graphs and in re-constructing causal structures. In recent years, the most state-of-art research in causal inference starts unifying the different causal inference frameworks together. This survey aims to provide a review of the past work on causal inference, focusing mainly on potential outcomes framework and causal graphical models. We hope that this survey will help accelerate the understanding of causal inference in different domains.