# Towards Efficient Local Causal Structure Learning

arXiv: https://arxiv.org/abs/2103.00378 · HTML: https://mlsanity.com/paper/2103.00378/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 28 Feb 2021.

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

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/1906.04477/) (2019)
- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs](https://mlsanity.com/paper/1904.11088/) (2019)
- [Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models](https://mlsanity.com/paper/1905.10857/) (2019)
- [A Unified View of Causal and Non-causal Feature Selection](https://mlsanity.com/paper/1802.05844/) (2018)
- [Testing Conditional Independence on Discrete Data using Stochastic Complexity](https://mlsanity.com/paper/1903.04829/) (2019)
- [Causal Discovery with Cascade Nonlinear Additive Noise Models](https://mlsanity.com/paper/1905.09442/) (2019)

## Built on by (most cited first, 7 of 7)

- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Towards Privacy-Aware Causal Structure Learning in Federated Setting](https://mlsanity.com/paper/2211.06919/) (2022)
- [Local Causal Structure Learning in the Presence of Latent Variables](https://mlsanity.com/paper/2405.16225/) (2024)
- [Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data](https://mlsanity.com/paper/2206.08448/) (2022)
- [Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing](https://mlsanity.com/paper/2409.12797/) (2024)
- [Hybrid Local Causal Discovery](https://mlsanity.com/paper/2412.19507/) (2024)
- [From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables](https://mlsanity.com/paper/2604.22416/) (2026)

## Abstract (arXiv)

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.