D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
ACM Computing Surveys2022Vol. 55(4), pp. 1–36
Citations Over TimeTop 1% of 2022 papers
Abstract
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.
Related Papers
- → Causality in the Sciences(2011)198 cited
- → Inferring Hidden Causal Structure(2010)48 cited
- → Causal inference with observational data(2022)9 cited
- → Confidence in causal inference under structure uncertainty in linear causal models with equal variances(2023)4 cited
- → Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances(2023)