# Causal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer

arXiv: https://arxiv.org/abs/2602.03914 · HTML: https://mlsanity.com/paper/2602.03914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model](https://mlsanity.com/paper/2502.09533/) (2025)
- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [IMAGHarmony: Controllable Image Editing with Consistent Object Quantity and Layout](https://mlsanity.com/paper/2506.01949/) (2025)
- [Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion](https://mlsanity.com/paper/1705.09031/) (2017)
- [Multiple Quantitative Trait Analysis Using Bayesian Networks](https://mlsanity.com/paper/1402.2905/) (2014)
- [SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee](https://mlsanity.com/paper/1707.01283/) (2017)
- [Partitioned hybrid learning of Bayesian network structures](https://mlsanity.com/paper/2103.12188/) (2021)
- [Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning](https://mlsanity.com/paper/2406.06348/) (2024)

## Abstract (arXiv)

This paper tackles a critical bottleneck in Super-Structure-based divide-and-conquer causal discovery: the high computational cost of constructing accurate Super-Structures--particularly when conditional independence (CI) tests are expensive and domain knowledge is unavailable. We propose a novel, lightweight framework that relaxes the strict requirements on Super-Structure construction while preserving the algorithmic benefits of divide-and-conquer. By integrating weakly constrained Super-Structures with efficient graph partitioning and merging strategies, our approach substantially lowers CI test overhead without sacrificing accuracy. We instantiate the framework in a concrete causal discovery algorithm and rigorously evaluate its components on synthetic data. Comprehensive experiments on Gaussian Bayesian networks, including magic-NIAB, ECOLI70, and magic-IRRI, demonstrate that our method matches or closely approximates the structural accuracy of PC and FCI while drastically reducing the number of CI tests. Further validation on the real-world China Health and Retirement Longitudinal Study (CHARLS) dataset confirms its practical applicability. Our results establish that accurate, scalable causal discovery is achievable even under minimal assumptions about the initial Super-Structure, opening new avenues for applying divide-and-conquer methods to large-scale, knowledge-scarce domains such as biomedical and social science research.