D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

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.

• Publication year

  2021

• Venue

  ACM Computing Surveys

• Publication date

  2021-03-03

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1145/3527154arXiv 2103.02582
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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