A Survey on Causal Discovery: Theory and Practice

TL;DR

This survey reviews recent advancements in causal discovery, discussing algorithms, tools, data, and applications to understand how causal graphs can be recovered from data for scientific and practical purposes.

Contribution

It provides a comprehensive, unified overview of existing causal discovery algorithms, tools, and applications, highlighting recent progress and practical insights.

Findings

Overview of various causal discovery algorithms

Compilation of useful tools and datasets

Real-world applications demonstrating effectiveness

Abstract

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.