A Survey on Causal Discovery: Theory and Practice

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.

References in corpus (8)

• Distinguishing cause from effect using observational data: methods and benchmarks
• Learning high-dimensional directed acyclic graphs with latent and selection variables
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
• CAM: Causal additive models, high-dimensional order search and penalized regression
• A fast PC algorithm for high dimensional causal discovery with multi-core PCs
• Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
• Joint Causal Inference from Multiple Contexts
• backShift: Learning causal cyclic graphs from unknown shift interventions

Cited by in corpus (6)

• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Treatment Effect Estimation for User Interest Exploration on Recommender Systems
• Causal Inference with Latent Variables: Recent Advances and Future Prospectives
• Visual Analysis of Multi-outcome Causal Graphs
• Learning Linear Gaussian Polytree Models with Interventions
• Symmetric observations without symmetric causal explanations