# A Review on Algorithms for Constraint-based Causal Discovery

arXiv: https://arxiv.org/abs/1611.03977 · HTML: https://mlsanity.com/paper/1611.03977/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2016.

## Built on by (most cited first, 8 of 8)

- [Causal Discovery for Fairness](https://mlsanity.com/paper/2206.06685/) (2022)
- [ML4C: Seeing Causality Through Latent Vicinity](https://mlsanity.com/paper/2110.00637/) (2021)
- [Causality, Causal Discovery, and Causal Inference in Structural Engineering](https://mlsanity.com/paper/2204.01543/) (2022)
- [Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning](https://mlsanity.com/paper/2502.10883/) (2025)
- [Test-Time Learning of Causal Structure from Interventional Data](https://mlsanity.com/paper/2602.19131/) (2026)
- [Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community](https://mlsanity.com/paper/2110.07938/) (2021)
- [Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge](https://mlsanity.com/paper/2407.20506/) (2024)
- [DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data](https://mlsanity.com/paper/2607.09348/) (2026)

## Abstract (arXiv)

Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. During the past decades, significant amount of progresses have been made toward this fundamental data mining paradigm. Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency. In this paper, we aim to review the constraint-based causal discovery algorithms. Firstly, we discuss the learning paradigm of the constraint-based approaches. Secondly and primarily, the state-of-the-art constraint-based casual inference algorithms are surveyed with the detailed analysis. Thirdly, several related open-source software packages and benchmark data repositories are briefly summarized. As a conclusion, some open problems in constraint-based causal discovery are outlined for future research.