# Accelerating Recursive Partition-Based Causal Structure Learning

arXiv: https://arxiv.org/abs/2102.11545 · HTML: https://mlsanity.com/paper/2102.11545/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [Learning Equivalence Classes of Bayesian Networks Structures](https://mlsanity.com/paper/1302.3566/) (2013)
- [SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee](https://mlsanity.com/paper/1707.01283/) (2017)
- [On a hypergraph probabilistic graphical model](https://mlsanity.com/paper/1811.08372/) (2018)
- [AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms](https://mlsanity.com/paper/2002.10870/) (2020)
- [Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs](https://mlsanity.com/paper/2002.12001/) (2020)
- [Speeding Up Distributed Pseudo-tree Optimization Procedure with Cross Edge Consistency to Solve DCOPs](https://mlsanity.com/paper/1909.06537/) (2019)
- [Order-Independent Structure Learning of Multivariate Regression Chain Graphs](https://mlsanity.com/paper/1910.01067/) (2019)
- [Learning LWF Chain Graphs: A Markov Blanket Discovery Approach](https://mlsanity.com/paper/2006.00970/) (2020)

## Built on by (most cited first, 4 of 4)

- [Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables](https://mlsanity.com/paper/2208.06935/) (2022)
- [An Expectation-Maximization Algorithm for Domain Adaptation in Gaussian Causal Models](https://mlsanity.com/paper/2601.03459/) (2026)
- [Efficient Causal Structure Learning via Modular Subgraph Integration](https://mlsanity.com/paper/2601.21014/) (2026)
- [A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables](https://mlsanity.com/paper/2605.10651/) (2026)

## Abstract (arXiv)

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recursive causal discovery algorithms have gained particular attention among the research community due to their ability to provide good results by using Conditional Independent (CI) tests in smaller sub-problems. However, each of such algorithms needs a refinement function to remove undesired causal relations of the discovered graphs. Notably, with the increase of the problem size, the computation cost (i.e., the number of CI-tests) of the refinement function makes an algorithm expensive to deploy in practice. This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems. We theoretically prove the correctness of our algorithm. We then empirically evaluate its performance against the state-of-the-art algorithms in terms of solution quality and completion time in synthetic and real datasets.